The Open Radio Access Network (O-RAN) represents a significant advancement in cellular networks, promoting openness, intelligence, and flexibility in 5G deployment. However, designing a scheduler for 5G O-RAN presents significant challenges due to its unique network architecture, large scheduling space, and stringent timing requirements of various control loops. Existing model-based and model-free schedulers both have inherent drawbacks that hinder their performance and adoption in 5G O-RAN. Model-based schedulers struggle because accurately modeling wireless system is often impossible, and they usually suffer from high complexity due to the NP-hard problem structure and large scheduling space. On the other hand, model-free schedulers often face convergence issue under a large scheduling space, and they usually cannot provide performance guarantees or even satisfy constraints. In this paper, we present MOGUL—a MOdel-GUided Learning approach that retains the strengths of both model-based and model-free methods while avoiding their pitfalls. MOGUL employs a model-based optimization problem to derive a reduced yet promising scheduling space, which is then used as the action space for model-free online Deep Multi-agent Reinforcement Learning (DMARL) to determine the final scheduling decision. Moreover, MOGUL is specifically tailored to the O-RAN architecture, allowing seamless integration into various control loops while meeting their stringent timing requirements. Experimental results demonstrate that MOGUL outperforms both state-of-the-art model-based and model-free algorithms.

Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.

Federated learning enables decentralized model training while preserving data privacy. However, since the learning process overlays the physical network infrastructure, the efficiency of learning can be impacted by network connectivity. In this work, we conducted extensive experiments to empirically characterize the impacts and leverage the insights to propose an adaptive federation framework, where clients with limited bandwidth are only prompted to transmit adaptively compressed gradient updates when the gradient similarity score is similar between the local and global models. Our evaluation in simulated environments and on real hardware devices shows bandwidth savings of 60% to 78% compared to state-of-the-art methods.

Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have shown remarkable stealthiness, as they compromise model predictions only when inputs contain specific triggers. As a countermeasure, anomaly detection is widely used to filter out backdoor attacks in FL. However, the non-independent and identically distributed (non-IID) data distribution nature of FL clients presents substantial challenges in backdoor attack detection, as the data variety introduces variance among benign models, making them indistinguishable from malicious ones. In this work, we propose a novel distribution-aware backdoor detection mechanism, BoBa, to address this problem. To differentiate outliers arising from data variety versus backdoor attacks, we propose to break down the problem into two steps: clustering clients utilizing their data distribution, and followed by a voting-based detection. We propose a novel data distribution inference mechanism for accurate data distribution estimation. To improve detection robustness, we introduce an overlapping clustering method, where each client is associated with multiple clusters, ensuring that the trustworthiness of a model update is assessed collectively by multiple clusters rather than a single cluster. Through extensive evaluations, we demonstrate that BoBa can reduce the attack success rate to lower than 0.001 while maintaining high main task accuracy across various attack strategies and experimental settings.

Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, Ning Zhang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

With the proliferation of LEO satellite communications (SatCom) serving rural areas, there is a strong interest on exploring the performance limit (e.g., throughput) with such a service. This problem is challenging due to highly dynamic satellite positions, limited satellite beams and spectrum bandwidth, and wide disparity in number of subscribers across a vast area. Most existing analytical models fail to capture real-world characteristics of operational satellite network, such as long time interval between satellite handover and polarization in transmission. This paper makes a major step in advancing this research area by formalizing an analytical framework for LEO SatCom based on real-world satellite network. Our analytical framework addresses architectural issues such as gateway service region (GSR) and scheduling problems such as satellite/beam/channel-to-cell allocation, interference issues such as co-channel interference avoidance, polarization, and performance issues such as throughput fairness. Simulation results on a real-world satellite ephemeris (Starlink) show that the optimal scheduling solution based on our analytical framework can offer 93% scheduling efficiency while satisfying all design requirements and system constraints.

The O-RAN architecture introduces unprecedented flexibility and openness into modern cellular networks, allowing mix-and-match of components from different vendors and the rapid deployment of innovative solutions across the RAN vertical. Despite its openness, some fundamental technical challenges associated with 5G/Next-G still remain in O-RAN. A well known example is joint optimization of Resource Block (RB) allocation, Modulation and Coding Scheme (MCS) selection, and Beamforming (BF) design. In this paper, we present Savitar—an O-RAN scheduler that jointly optimizes these components, with the objective of minimizing spectrum usage while meeting per-UE probabilistic data rate requirements. Following the multi-timescale design principle in O-RAN, we present three components (each at a different time scale) of Savitar that can be seamlessly integrated with O-RAN RICs: (i) hyperparameter tuning in the Non-Real-Time (Non-RT) RIC, (ii) parallel RB Group (RBG) allocation and MCS selection in the Near-RT RIC, and (iii) BF vector design in the RT Open Distributed Unit (O-DU). A unique design in these components is our handling of CSI uncertainty with limited data samples. Experimental results show that Savitar achieves competitive spectrum efficiency performance while meeting our design requirements (i.e., per-UE probabilistic data rate requirement and real-time requirement in O-DU).

The scarcity of spectrum and the proliferation of data-intensive applications in 5G/Next-G networks call for innovations of new techniques that are capable of offering UE-level data rate guarantee with minimum required spectrum usage. This is a challenging problem due to the complexity of mechanisms involved in the process, such as Resource Block (RB) allocation, modulation and coding scheme (MCS) selection, and MU-MIMO beamforming (BF) design. Further complicating the problem is the random, unknown nature of Channel State Information (CSI) and the errors involved in its estimation. In this paper, we present Rudra, which offers a comprehensive solution to these challenges. Rudra formulates the bandwidth minimization problem by incorporating probabilistic data rate guarantee through a chance constraint, which embeds RB allocation, MCS selection, and MU-MIMO BF mechanisms. The CSI uncertainty problem is addressed through a novel error-embedded (EE)-Wasserstein ambiguity set based on a small set of data samples. We show that the solution by Rudra meets our design objective and outperforms a modified state-of-the-art algorithm.

We study an Age-of-Information (AoI) scheduling problem where users can tolerate occasional violations of AoI for each source at the base station. Each user’s AoI is associated with a violation tolerance constraint. We are interested in determining whether a set of users, each with a given AoI deadline, a violation tolerance constraint, and a packet loss rate (due to channel condition) is schedulable, and if so, find a feasible scheduler. For this problem, we study two cases: 1) the stable tolerant case where the tolerance rate is higher than the packet loss rate for each source and 2) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For the stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when the system load is no greater than$\ln 2$(roughly 70%). When the system load is between$\ln 2$and 1, we offer a necessary and sufficient condition for STS to find a feasible scheduler by solving an optimization problem. Likewise, for the unstable tolerance case, we develop a scheduler called unstable tolerant scheduler (UTS) and its corresponding schedulability conditions. Through extensive simulations, we show that STS and UTS match our theoretical results.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to AoI-related problems. These problems vary in specific AoI-based objectives and network settings. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we envision and pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization and decision problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve three important problems: to minimize weighted sum of AoIs, to minimize bandwidth requirement under AoI constraints, and to determine the existence of feasible schedulers to satisfy AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new (or general) results that are not available in the literature.

Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, especially misbehaviors, necessitating a robust Misbehavior Detection System (MBDS). While Machine Learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, Generative Adversarial Networks (GAN) possess the potential to mitigate the aforementioned issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS that detects any misbehavior and ensures robustness against adversarial perturbation. In this article, we present several key contributions. First, we propose an advanced threat model for stealthy V2X misbehavior where the attacker can transmit malicious data and mask it using adversarial attacks to avoid detection by ML-based MBDS. We formulate two categories of adversarial attacks against the anomaly-based MBDS. Later, in the pursuit of a generalized and robust GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and present Ve hicular GAN ( VehiGAN ), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance. We present a physics-guided data preprocessing technique that generates effective features for ML-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors, VehiGAN outperforms the baseline and exhibits comparable detection performance in other scenarios. Particularly, VehiGAN excels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover, VehiGAN provides approximately 92% improvement in false positive rate under powerful adaptive adversarial attacks, and possesses intrinsic robustness against other adversarial attacks that target the false negative rate. Finally, we make the data and code available for reproducibility and future benchmarking, available at https://github.com/shahriar0651/VehiGAN .

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of adequate security in IoT devices renders IoT systems vulnerable to various network-based cyberattacks, potentially causing severe damage. Recent works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than raw local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. In addition, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset and the BaIoT dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-independent and identically distributed (non-IID) data. Our implementation of FeCo on a Raspberry Pi device further confirms the applicability of FeCo for resource-constrained IoT devices.

Federated learning (FL) has been shown vulnerable to a new class of adversarial attacks, known asmodel poisoning attacks (MPA), where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. Existing defenses that have been fixated on analyzing model parameters show limited effectiveness in detecting such malicious models. In this work, we proposeFLARE, a robust model aggregation mechanism for FL, which is resilient against state-of-the-art MPAs. Instead of solely depending on model parameters,FLAREleverages thepenultimate layer representations (PLRs)of the model for characterizing the adversarial influence on each local model update. We further propose a trust evaluation method that estimates a trust score for each model update based on pairwise PLR discrepancies among all model updates. Under the assumption of honest majority,FLAREassigns a low trust score to model updates that are far from the benign cluster.FLAREthen aggregates the model updates weighted by their trust scores and finally updates the global model. Extensive experimental results demonstrate the effectiveness ofFLAREin defending FL against various MPAs, including semantic backdoor attacks, trojan backdoor attacks, and untargeted attacks, in various FL systems.

MU-MIMO beamforming is a key technology for 5G networks, relying on Channel State Information (CSI). However, in practice, the estimated CSI in reality is prone to uncertainty. Further, a MU-MIMO beamforming solution must be derived within a millisecond to be useful for real-time 5G applications. We present ReDBeam—a real-time data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main novelties of ReDBeam are a parallel algorithm and an optimized GPU implementation. ReDBeam delivers a MU-MIMO beamforming solution within 1 millisecond to meet the probabilistic data rate requirements from the users, and minimize a base station’s power consumption. Through extensive experiments, we show that ReDBeam consistently meets the stringent 1-millisecond real-time requirement and is orders of magnitude faster than other state-of-the-art algorithms. ReDBeam conclusively demonstrates that MU-MIMO beamforming with data rate requirements can be achieved in real-time using only limited CSI data samples.

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

Blockchain’s decentralization, transparency, and tamper-resistance properties have facilitated the system’s use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of Blockchain. Many solutions have been proposed to address these issues, including first-layer solutions (or on-chain solutions) and second-layer solutions (or off-chain solutions). Among the proposed solutions, the blockchain sharding system is the most scalable one, where the nodes in the network are divided into several groups. The nodes in different shards work in parallel to validate the transactions and add them to the blocks, and in such a way, the throughput increases significantly. However, previous works have not adequately summarized the latest achievements in blockchain sharding, nor have they fully showcased its state-of-the-art. Our study provides a systemization of knowledge of public blockchain sharding, including the core components of sharding systems, challenges, limitations, and mechanisms of the latest sharding protocols. We also compare their performance and discuss current constraints and future research directions.

MU-MIMO beamforming is a key technology for 5G/NextG networks. In practice, MU-MIMO beamforming requires Channel State Information (CSI) and is prone to uncertainty. Furthermore, a beamforming solution must be derived within a millisecond (ms) to be useful for real-time (RT) 5G applications. We present ReDBeam-a RT data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main contribution of ReDBeam is a parallel algorithm and an optimized GPU implementation. ReDBeam minimizes the base station (BS)'s power consumption while offering a probabilistic guarantee of users' data rates. It is purposefully designed to take advantage of the vast parallel processing capability in commercial off-the-shelf GPUs. Through extensive experiments, we show that ReDBeam can meet the 1 ms RT requirement and is orders of magnitude faster than other state-of-the-art algorithms for the same problem.

Multiplexing Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface poses significant challenges due to extreme latency requirement of URLLC packets. This paper investigates the direct puncturing of URLLC traffic over eMBB transmissions, a method that, while guaranteeing immediate URLLC packet delivery, can severely degrade eMBB performance. To alleviate the adverse impact on eMBB, we present Cyrus—a deep reinforcement learning (DRL)-based puncturing solution for eMBB and URLLC multiplexing. Cyrus is tailored for the Open RAN (O-RAN) architecture and unifies the three control loops of O-RAN synergistically in its design of DRL-based solution. Not only does Cyrus meet the real-time requirements for URLLC but also it continuously updates and improves its scheduling policy based on changing network conditions. The effectiveness of Cyrus is demonstrated through link-level simulations for 5G NR, showing significant improvement in eMBB performance over the state-of-the-art, particularly as URLLC traffic increases.

Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, necessitating a robust misbehavior detection system (MBDS). While machine learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, generative adversarial networks (GAN) possess the potential to mitigate such issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS. Our contributions are manifold. In the pursuit of an effective GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and present VEhicular GAN (VEHIGAN), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance and adversarial robustness. We present a physics-guided data preprocessing technique that generates effective features for ML-based misbehavior detection. To evaluate the adversarial robustness, we formulate two categories of adversarial attacks against the WGAN-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors, VehigAnoutperforms the baselines and exhibits comparable detection performance in other scenarios. Particularly, VehigAnexcels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover, VehigAnprovides approximately 92% improvement in false positive rates under powerful adaptive adversarial attacks and possesses intrinsic robustness against other adversarial attacks that target false negative rates. Finally, we make the data and code available for reproducibility and future benchmarking, available at https://eithub.com/shahriar0651/VehiGAN.

Age of Information (AoI) is a promising metric to measure information freshness and optimizing AoI through scheduling is one of the most intensely studied areas in AoI research. To date, the vast majority of research on AoI scheduling has been based on simplified communication models that often fail to capture the complexities found in real-world network systems such as 5G. While there are some limited efforts on AoI scheduling that have ventured into exploring OFDMA-based data transmission models similar to those in 5G, they tend to neglect essential elements, such as channel-dependent resource block (RB) allocation and modulation and coding scheme (MCS) assignment, rendering limited utility to real-world 5G systems. In this article, we focus on developing 5G-compliant AoI schedulers. We study a specific problem with the objective of minimizing outdated information across all source nodes. This problem arises from practice where there is a specific information freshness requirement, known as AoI deadline for each source node. We present Aequitas, an innovative 5G scheduler designed to optimize this objective through joint RB allocation and MCS assignment, both of which are dependent on frequency and time-selective channel fading. We exploit a property called “uniform fairness,” derived from the analysis of an optimal offline scheduler, to develop Aequitas. To meet stringent timing requirement in 5G, Aequitas leverages the parallel computing capability of a commercial off-the-shelf GPU. Extensive evaluations demonstrate that Aequitas closely approaches the theoretical lower bound in terms of objective performance, while maintaining operational times below the 5G timing requirement.

Supporting ultra reliable low-latency communication (URLLC) is an extremely challenging problem, due to the excessive requirements on reliability and latency. To date, few of the existing research efforts have successfully addressed the ultra reliability problem for URLLC. This article investigates this problem through the design of a URLLC scheduler for industrial automation under the open radio access network (O-RAN) architecture. We cast the URLLC data transmission problem as a resource scheduling problem, where a set of resource blocks (RBs) from a set of O-RAN radio units (O-RUs) must be allocated to a set of user equipments (UEs) for information transmission. The challenge is to find a scheduling solution in each mini-slot (sub millisecond time scale) based on dynamic channel conditions and satisfy the ultra reliability requirement (e.g., 99.9999%, or six-nine). We present Pistis—a novel scheduler design that fully utilizes the three control loops in O-RAN. Pistis exploits channel slow fading and PHY-layer properties to reduce the search space in its design of the near-real time (near-RT) component. It further leverages GPU parallel computing in its design of the real time (RT) component, which takes into account of fast fading in channel dynamics. We implement Pistis on commercial off-the-shelf hardware and demonstrate that Pistis is able to meet the six-nine reliability requirement for 4 O-RUs, 40 RBs, and 40 UEs within 0.5 ms.

Open radio access network (O-RAN) enables cooperative signal processing among multiple cells at a centralized O-RAN distributed unit (O-DU). It is a key technology for cellular networks to increase spectrum efficiency. To achieve cooperative signal processing across multiple cells, a new scheduler is needed. Specifically, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users from all the cells that are involved in multi-cell processing. In addition, the scheduler must obtain its scheduling solution within each TTI (i.e., at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present O-$\mathbf M^{3}$—a real-time scheduler formulti-cellMIMO networks under the O-RAN architecture. O-$\mathbf M^{3}$can meet the stringent timing requirement with joint optimization of beamforming matrices, RB allocation, and MCS assignment among multiple cells. O-$\mathbf M^{3}$is developed through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is performed for cell-center users to explore MU-MIMO transmission. We implement O-$\mathbf M^{3}$on a commercial off-the-shelf (COTS) GPU. Experimental results show that O-$\mathbf M^{3}$is capable of offering a scheduling solution within 500$\mu \text{s}$for an O-RAN system with 7 O-RAN radio units (O-RUs), 100 users, 100 RBs, and$2\times 8$MIMO. O-$\mathbf M^{3}$can also meet the 1 ms requirement for$2\times 12$MIMO systems. Meanwhile, O-$\mathbf M^{3}$can provide ~40% throughput gain on average through joint transmission across multiple cells.

Channel State Information (CSI) is a critical piece of information for MU-MIMO beamforming. However, CSI estimation errors are inevitable in practice. The random and uncertain nature of CSI estimation errors poses significant challenges to MU-MIMO beamforming. State-of-the-art works addressing such a CSI uncertainty can be categorized into model-based and data-driven works, both of which have limitations when providing a performance guarantee to the users. In contrast, this paper presents Limited Sample-based Beamforming (LSBF)—a novel approach to MU-MIMO beamforming that only uses a limited number of CSI data samples (without assuming any knowledge of channel distributions). Thanks to the use of CSI data samples, LSBF enjoys flexibility similar to data-driven approaches and can provide a theoretical guarantee to the users—a major strength of model-based approaches. To achieve both, LSBF employs chance-constrained programming (CCP) and utilizes the$\infty $-Wasserstein ambiguity set to bridge the unknown CSI distribution with limited CSI samples. Through problem decomposition and a novel bilevel formulation for each subproblem based on limited CSI data samples, LSBF solves each subproblem with a binary search and convex approximation. We show that LSBF significantly improves the network performance while providing a probabilistic data rate guarantee to the users.

This paper investigates a bandwidth minimization problem with Age of Information (AoI) constraints—a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment while, at the same time, there is an expectation of AoI requirement on the application side. We present a novel polynomial-time algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure calledFractional Consecutively Divisible(FCD). In the general case when the given AoI constraint vector is not FCD, we show that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. We validate the performance of Aion through a large number of simulations and all results confirm our theoretical findings. The results from this paper lay a foundation for future research on bandwidth minimization with AoI guarantee.

Open Radio Access Network (O-RAN) offers a new paradigm for the design and deployment of future RANs. The unique architecture of O-RAN presents two main challenges when designing a scheduler. First, it is impractical to obtain accurate and full Channel State Information (CSI) due to estimation errors and limited bandwidth of the fronthaul link between Open Radio Unit (O-RU) and Open Distributed Unit (O-DU). Second, the large-scale processing at an O-DU introduces difficulties in meeting the stringent time requirement in O-RAN, especially in the real-time (RT) control loop. To address these challenges, we propose R3—a real-time robust Multi-user, Multiple Input, Multiple Output (MU-MIMO) scheduler for O-RAN. R3 serves as a comprehensive scheduling solution encompassing RB allocation, MCS selection, and beamforming calculation. Most notably, R3 utilizes a limited number of CSI samples to offer probabilistic QoS guarantees. To meet the timing requirements of O-RAN, R3 decomposes the scheduling problem into two distinct sub-problems and integrates them into separate control loops. Moreover, each sub-problem is designed with a parallel structure, utilizing a reduced search space, and implemented on a GPU platform to accelerate the computation time. Experimental results demonstrate that R3 offers competitive throughput performance as the state-of-the-art while simultaneously fulfilling the QoS guarantees. Further, R3 meets the timing requirements of various control loops in O-RAN over a wide range of operating conditions.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to various AoI-related optimization problems. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related optimization problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve two important AoI-related problems: to minimize the weighted sum of AoIs and to minimize the bandwidth requirement under AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new results that are not available in the literature.

When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the "truthfulness" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.

Time-sensitive applications, such as 5G and IoT, are imposing increasingly stringent security and reliability requirements on network time synchronization. Precision time protocol (PTP) is a de facto solution to achieve high precision time synchronization. It is widely adopted by many industries. Existing efforts in securing the PTP focus on the protection of communication channels, but little attention has been given to the threat of malicious insiders. In this paper, we first present the security vulnerabilities of PTP and discuss why the current defense mechanisms are unable to counter Byzantine insiders. We demonstrate how a malicious insider can spoof a time source to arbitrarily shift the system time of a victim node on an IoT testbed. We further demonstrate the harmful consequence of the attack on a real Turtlebot3 robotic platform as the robot fails to locate itself and follows a false trajectory. As a countermeasure, we propose multi-source PTP, in short, MS-PTP, a Byzantine-resilient network time synchronization mechanism that relies on time crowdsourcing. MS-PTP changes the current PTP's single source hierarchy to a multi-source client-server architecture, in which PTP clients take responses from multiple time servers and apply a novel secure aggregation scheme to eliminate the effect of malicious responses from unreliable sources. MS-PTP is able to counter f Byzantine failures when the total number of time sources n used by a client satisfies n>=3f+1. We provide rigorous proof for its non-parametric accuracy guarantee---achieving bounded error regardless of the Byzantine population. We implemented a prototype of MS-PTP on our IoT testbed and the results show its resilience against Byzantine insiders while maintaining high synchronization accuracy.

Recently, has been recognized that there is a practical limitation with the original notion of Age of Information (AoI) metric in terms of quantifying the freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. In this article, we introduce the notion of AoII+ metric by modifying AoII with practical considerations. Then, we investigate a scheduling problem to minimize AoII+ in an IoT data collection network. We derive a theoretical lower bound for the minimum AoII+. Then, we present Heh—a low-complexity online scheduler to minimize AoII+. The design of Heh is based on the estimation of a novel offline scheduling priority metric without any future knowledge. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes AoII+. Through extensive simulations, we show that the lower bound is very tight and that the AoII+ obtained by Heh is close to optimal.

Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control. With the expansion of advanced connectivity features in automobiles and the elevated risks of internal system exposure, the CAN bus is increasingly prone to intrusions and injection attacks. As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal/semantic level, while looking innocuous by the pattern/frequency of the CAN messages. The rule-based IDS, as well as the anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just the binary payload data and are less effective in detecting such attacks. Therefore, to detect such intelligent attacks, we propose CANShield, a deep learning-based signal-level intrusion detection framework for the CAN bus. CANShield consists of three modules: a data preprocessing module that handles the high-dimensional CAN data stream at the signal level and parses them into time series suitable for a deep learning model; a data analyzer module consisting of multiple deep autoencoder (AE) networks, each analyzing the time-series data from a different temporal scale and granularity, and finally an attack detection module that uses an ensemble method to make the final decision. Evaluation results on two high-fidelity signal-based CAN attack datasets show the high accuracy and responsiveness of CANShield in detecting advanced intrusion attacks.

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

Organizations tend to collaboratively train the deep learning model over their combined datasets for a common benefit (e.g., better-trained model or learning a complicated model). However, due to the consideration about privacy leakage, organizations cannot share their data directly, especially related to sensitive domains. In this paper, a privacy-preserving collaborative deep learning mechanism, namely Sigma, is designed to allow participating organizations to train a collective model without exposing their local training data to the others. Specifically, a single-server-aided private collaborative architecture is introduced to achieve the private collaborative learning, which protects organizations’ data even if$n-1$out of$n$participants colluded. We also design a practical protocol to perform the secure model training, which can resist the typical inference attack through the sharing information. After that, we propose a fair model releasing mechanism for participants and introduce differential privacy to prevent model stealing and membership inference attack. Furthermore, we prove that Sigma can ensure participants’ privacy preservation and analyze the communication overhead in theory. To evaluate the effectiveness and efficiency of Sigma, we conduct an experiment over two real-world datasets and the simulation results demonstrate that Sigma can efficiently achieve the collaborative model training and effectively resist the membership inference attack.

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks. One of the biggest challenges is that ML-based IDSs suffer from adversarial example (AE) attacks. By applying small perturbations (e.g., slightly increasing packet inter-arrival time) to the intrusion traffic, an AE attack can flip the prediction of a well-trained IDS. We address this challenge by proposingMANDA, a MANifold and Decision boundary-based AE detection system. Through analyzing AE attacks, we notice that 1) an AE tends to be close to its original manifold (i.e., the cluster of samples in its original class) regardless of which class it is misclassified into; and 2) AEs tend to be close to the decision boundary to minimize the perturbation scale. Based on the two observations, we designMANDAfor accurate AE detection by exploiting inconsistency between manifold evaluation and IDS model inference and evaluating model uncertainty on small perturbations. We evaluateMANDAon both binary IDS and multi-class IDS on two datasets (NSL-KDD and CICIDS) under three state-of-the-art AE attacks. Our experimental results show thatMANDAachieves high true-positive rate (98.41%) with a 5% false-positive rate.

Hybrid beamforming (HB) architecture has been widely considered for 5G mmWave systems. It reduces hardware complexity by allowing the number of RF chains to be far fewer than the number of antennas. A major practical challenge for HB is to obtain a beamforming solution in real-time. In 5G NR, new frame structures with short TTIs are employed to support mmWave communications. Under such frame structures, it is necessary to obtain a beamforming solution with a time resolution varying from 1 ms to 125$\mu$s – an extremely stringent time requirement considering the complexity involved in HB. In this paper, we present the design and implementation ofTurbo-HB– a novel beamforming design under the HB architecture that is capable of offering the beamforming matrices in less than 500$\mu$s. The key ideas of Turbo-HB include: (i) reducing the complexity of computation-intensive SVD operations by exploiting channel sparsity at mmWave frequencies, and (ii) achieving large-scale parallel computation with minimal memory access. We implement Turbo-HB on an off-the-shelf Nvidia GPU and conduct extensive experiments. Our experimental results demonstrate that Turbo-HB can obtain a beamforming solution in 500$\mu$s for up to 100 RBs and 10 MU-MIMO users on each RB while offering competitive throughput performance compared to state-of-the-art (non-real-time) algorithms.

Degree-of-freedom (DoF)-based models have been proven to be highly successful in modeling and analysis of MIMO systems. Among existing DoF-based models, the number of DoFs used for interference cancellation (IC) is solely based on the number of interfering data streams. However, from both experimental and simulation results, we find that signal strengths of an interference link vary significantly in different directions in the eigenspace. In this paper, we exploit the difference in interference signal strengths in the eigenspace and perform IC with DoFs only on those directions with strong signals. To differentiate interference signal strengths on an interference link, we introduce a novel concept called “effective rank threshold.” Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. To ensure the new DoF IC model is feasible at PHY layer, we propose an algorithm to set the weights for all nodes that can offer our desired DoF allocation.

Multi-User (MU)-MIMO technology plays a vital role in 5G NR. Under MU-MIMO transmission, multiple users can share the same time-frequency resources simultaneously. For 5G MU-MIMO systems, it is challenging to design a scheduler. The scheduler needs to determine resource block (RB) allocation, the number of data streams and modulation and coding scheme (MCS) for each user in each transmission time interval (TTI). In particular, multiple users can be co-scheduled on the same RB for MU-MIMO transmission. In addition, it is necessary for the scheduler to find a scheduling solution within each TTI to be useful. In this paper, we present mCore$+$, a novel design and implementation that can achieve$\sim$500$\mu$s timing performance for 5G MU-MIMO systems. mCore$+$is meticulously designed with a multi-phase optimization and heavily leverages large-scale parallel computation. In each phase, mCore$+$either decomposes the optimization problem into a number of independent sub-problems, or reduces the search space into a smaller but most promising subspace, or both. mCore$+$is validated on a commercial-off-the-shelf GPU platform. Experimental results show that mCore$+$can offer a scheduling solution in$\sim$500$\mu$s for up to 100 RBs, 100 users, 29 MCS levels and$4 \times 12$MIMO systems. Also, mCore$+$can achieve better or comparable throughput performance compared to other state-of-the-art algorithms.

Underlay coexistence is a spectrum efficient mechanism to roll out 5G picocells within a macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocells, it is impossible to have complete knowledge of channel conditions between them. Under such a circumstance, chance-constrained programming (CCP) has been shown to be an ideal optimization tool to address such a channel uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G’s timing requirement (125$\mu s$). To address this problem, we propose a novel scheduler called GPU-based Underlay Coexistence (GUC) with the goal of finding an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small subproblems that are suitable for parallel computation on GPU platforms. By selecting a subset of promising subproblems and solving them in parallel with fast algorithms, we are able to leverage GPU parallel computing and develop a real-time solution. Through extensive experiments, we show that GUC meets the 125$\mu s$requirement while achieving 90% optimality on average.

Federated meta-learning has emerged as a promising AI framework for today’s mobile computing scenes involving distributed clients. It enables collaborative model training using the data located at distributed mobile clients and accommodates clients that need fast model customization with limited new data. However, federated meta-learning solutions are susceptible to inference-based privacy attacks since the global model encoded with clients’ training data is open to all clients and the central server. Meanwhile, differential privacy (DP) has been widely used as a countermeasure against privacy inference attacks in federated learning. The adoption of DP in federated meta-learning is complicated by the model accuracy-privacy trade-off and the model hierarchy attributed to the meta-learning component. In this paper, we introduce DP-FedMeta, a new differentially private federated meta-learning architecture that addresses such data privacy challenges. DP-FedMeta features an adaptive gradient clipping method and a one-pass meta-training process to improve the model utility-privacy trade-off. At the core of DP-FedMeta are two DP mechanisms, namely DP-AGR and DP-AGRLR, to provide two notions of privacy protection for the hierarchical models. Extensive experiments in an emulated federated meta-learning scenario on well-known datasets (Omniglot, CIFAR-FS, and Mini-ImageNet) demonstrate that DP-FedMeta accomplishes better privacy protection while maintaining comparable model accuracy compared to the state-of-the-art solution that directly applies DP-based meta-learning to the federated setting.

Federated learning (FL) has been shown vulnerable to a new class of adversarial attacks, known as model poisoning attacks (MPA), where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. Existing defenses that have been fixated on analyzing model parameters show limited effectiveness in detecting such carefully crafted poisonous models. In this work, we propose FLARE, a robust model aggregation mechanism for FL, which is resilient against state-of-the-art MPAs. Instead of solely depending on model parameters, FLARE leverages the penultimate layer representations (PLRs) of the model for characterizing the adversarial influence on each local model update. PLRs demonstrate a better capability to differentiate malicious models from benign ones than model parameter-based solutions. We further propose a trust evaluation method that estimates a trust score for each model update based on pairwise PLR discrepancies among all model updates. Under the assumption that honest clients make up the majority, FLARE assigns a trust score to each model update in a way that those far from the benign cluster are assigned low scores. FLARE then aggregates the model updates weighted by their trust scores and finally updates the global model. Extensive experimental results demonstrate the effectiveness of FLARE in defending FL against various MPAs, including semantic backdoor attacks, trojan backdoor attacks, and untargeted attacks, and safeguarding the accuracy of FL.

Cloud Radio Access Network (C-RAN) is a novel centralized architecture for cellular networks. C-RAN can significantly improve spectrum efficiency by performing cooperative signal processing for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed before we can take advantage of C-RAN's multi-cell processing capability. Under C-RAN architecture, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under all covering cells. In addition, it is necessary to obtain a scheduling solution within each TTI (at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present M3—a sub-ms scheduler for multi-cell MIMO networks under C-RAN architecture. M3addresses the stringent timing requirement through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. Experimental results show that M3is capable of offering a scheduling solution within 1 ms for 7 remote radio heads (RRHs), 100 users, 100 RBs, and 2×12 MIMO. Meanwhile, M3provides ~40%. throughput gain on average by employing joint transmission.

Accurate estimation of Channel State Information (CSI) is essential to design MU-MIMO beamforming. However, errors in CSI estimation are inevitable in practice. State-of-the-art works model CSI as random variables and assume certain specific distributions or worst-case boundaries, both of which suffer performance issues when providing performance guarantees to the users. In contrast, this paper proposes a Data-Driven Beamforming (D2BF) that directly handles the available CSI data samples (without assuming any particular distributions). Specifically, we employ chance-constrained programming (CCP) to provide probabilistic data rate guarantees to the users and introduce ∞-Wasserstein ambiguity set to bridge the unknown CSI distribution with the available (limited) data samples. Through problem decomposition and a novel bilevel formulation for each subproblem, we show that each subproblem can be solved by binary search and convex approximation. We also validate that D2BF offers better performance than the state-of-the-art approach while meeting probabilistic data rate guarantees to the users.

Recently, it has been recognized that there is a serious limitation with the original Age of Information (AoI) metric in terms of quantifying true freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. By further refining this new metric with practical considerations, we introduce Age of Outdated Information (Ao2I) metric. In this paper, we investigate a scheduling problem for minimizing Ao2I in an IoT data collection network. We derive a theoretical lower bound for the minimum Ao2I that any scheduler can achieve. Then we present Heh—a low-complexity online scheduler. The design of Heh is based on the estimation of a novel offline scheduling priority metric in the absence of knowledge of the future. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes Ao2I. Through extensive simulations, we show that the lower bound is very tight and that the Ao2I obtained by Heh is close-to-optimal.

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of sufficient security features in IoT devices renders IoT ecosystems vulnerable to various network intrusion attacks, potentially causing severe damage. Previous works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. Besides, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-IID data. Evaluations on convergence, computation overhead, and scalability further confirm the suitability of FeCo for IoT intrusion detection.

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ($< 125 \mu \text{s}$) in 5G NR.

Edge node placement is a key topic to edge cloud systems for that it affects their service performances significantly. Previous solutions based on the existing information are not suitable for the mobile environment due to the mobility and random Internet access of end users. In this article, we propose a dynamic virtual edge node placement scheme, in which the edge node placement strategy is generated based on the prediction information. Our placement scheme applies the pay-as-you-go and Spot Instance model of cloud computing, which may allocate the service resources with low cost conveniently and flexibly. What’s more, Long Short-Term Memory (LSTM) is implemented to predict the information of end users’ requests and the resources’ prices, endowing the generated placement strategy with the adaptability to the change of end users. At last, a set of hierarchical-clustering-based placement algorithms are proposed, which not only locate virtual edge nodes and allocate their corresponding service resources actively, but also guarantee the service quality of end users with low time complexity. The simulation with trace data shows that compared with K-means-clustering-based placement schemes, our virtual edge node placement scheme can provide users with high-quality service in terms of network delay with relatively low placement cost time-efficiently.

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today’s differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this article, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection. While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency. Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure. The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples. Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine. We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets. Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions. Thus, we avoid the inefficiency of FHE based paradigm.

5G NR is designed to operate over a broad range of frequency bands and support new applications with ultra-low latency requirements. To support its extremely diverse operating conditions, multiple OFDM numerologies have been defined in the 5G standards. Under these numerologies, it is necessary to perform scheduling with a time resolution of$\sim 100 \mathrm {\mu s}$. This requirement poses a new challenge beyond existing LTE and cannot be satisfied by any existing LTE schedulers. In this paper, we present the design of GPF+, which is a GPU-based proportional fair (PF) scheduler with timing performance under$100 \mathrm {\mu s}$. GPF+ is an improvement over our GPF in Huanget al.(2018). The key ideas include decomposing the original scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF+ on an off-the-shelf NVIDIA Tesla V100 GPU, we show that GPF+ is able to achieve near-optimal PF performance with timing performance under$100 \mathrm {\mu s}$. GPF+ represents the fastest GPU-based PF scheduler that can meet the new real-time requirement in 5G NR.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure calledCyclic Scheduler Detection(CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, calledFictitious Polynomial Mapping(FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under$\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

We study the problem of channel uncertainty on wireless transmissions from different users with mutual interference. Specifically, the channel gains from the transmitters to the receivers are available only through their mean and covariance rather than complete distributions. Our goal is to maximize the energy efficiency among all transmitter-receiver pairs while guaranteeing their capacity requirements. For this problem, we employ chance-constrained programming (CCP), which allows occasional violation of target capacity threshold as long as the probability of such violation is below a small tolerable constant (risk level). We propose a solution based on a novel reformulation technique that converts the original CCP into a deterministic optimization problem without relaxation errors. Then the deterministic optimization problem is approximated into a Geometric Program (GP) based on tight polynomial approximations, which can be solved optimally. We prove that our proposed solution achieves near-optimal performance with polynomial time complexity.

URLLC is an important use case in 5G NR that targets at 1-ms level delay-sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by eMBB service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through MCS selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE - the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implement DELUXE through a link-level 5G NR simulator. Extensive experimental results show that DELUXE can successfully maintain the desired link reliability for eMBB while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement (<; 125 μs) in 5G NR.

MU-MIMO technology enables a base station (BS) to transmit signals to multiple users simultaneously on the same frequency band. It is a key technology for 5G NR to increase the data rate. In 5G specifications, an MU-MIMO scheduler needs to determine RBs allocation and MCS assignment to each user for each TTI. Under MU-MIMO, multiple users may be coscheduled on the same RB and each user may have multiple data streams simultaneously. In addition, the scheduler must meet the stringent real-time requirement (~1 ms) during decision making to be useful. This paper presents mCore, a novel 5G scheduler that can achieve ~1 ms scheduling with joint optimization of RB allocation and MCS assignment to MU-MIMO users. The key idea of mCore is to perform a multi-phase optimization, leveraging large-scale parallel computation. In each phase, mCore either decomposes the optimization problem into a number of independent sub-problems, or reduces the search space into a smaller but most promising subspace, or both. We implement mCore on a commercial-off-the-shelf GPU. Experimental results show that mCore can offer the best scheduling performance for up to 100 RBs, 100 users, 29 MCS levels and 4 × 12 antennas when compared to other state-of-the-art algorithms. It is also the only algorithm that can find its scheduling solution in ~1 ms.

We study an Age of Information (AoI) scheduling problem where AoI for each source at the base station (BS) can tolerate occasional violations, which we define as a violation tolerance constraint. The problem is to determine whether a set of users with given AoI deadlines, tolerance rates, and packet loss rates (due to each source's channel condition) is schedulable, and if so find a feasible scheduler. We study two cases: (i) the stable tolerant case where the tolerance rate is higher than the packet loss rate for all sources; (ii) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when system load is no greater than ln 2. For unstable tolerance case, we develop unstable tolerant scheduler (UTS) and identify a schedulability condition for it. Through extensive simulations, we show that STS and UTS match our theoretical results.

This paper investigates bandwidth minimization under AoI constraints - a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment when AoI is used as a constraint. We present a novel fast algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure called Fractional Consecutively Divisible (FCD). In the general case when the given AoI constraint vector is not FCD, we prove that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. The results from this paper lay the foundation for future research on bandwidth minimization with AoI guarantee.

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks. Yet one of the biggest challenges is that ML-based IDSs suffer from adversarial example (AE) attacks. By applying small perturbations (e.g. slightly increasing packet inter-arrival time) to the intrusion traffic, an AE attack can flip the prediction of a well-trained IDS. We address this challenge by proposing MANDA, a MANifold and Decision boundary-based AE detection system. Through analyzing AE attacks, we notice that 1) an AE tends to be close to its original manifold (i.e., the cluster of samples in its original class) regardless which class it is misclassified into; and 2) AEs tend to be close to the decision boundary so as to minimize the perturbation scale. Based on the two observations, we design MANDA for accurate AE detection by exploiting inconsistency between manifold evaluation and IDS model inference and evaluating model uncertainty on small perturbations. We evaluate MANDA on NSL-KDD under three state-of-the-art AE attacks. Our experimental results show that MANDA achieves as high as 98.41% true-positive rate with 5% false-positive rate and can be applied to other problem spaces such as image recognition.

Mobile Edge Computing (MEC) has emerged to be an integral component of 5G infrastructure due to its potential to speed up task processing and reduce energy consumption for mobile devices. However, a major technical challenge in making offloading decisions is that the number of required processing cycles of a task is usually unknown in advance. Due to this processing uncertainty, it is difficult to make offloading decisions while providing any guarantee on task deadlines. To address this challenge, we propose EPD---Energy-minimized solution with Probabilistic Deadline guarantee to task offloading problem. The mathematical foundation of EPD is Exact Conic Reformulation (ECR), which is a powerful tool that reformulates a probabilistic constraint for task deadline into a deterministic one. In the absence of distribution knowledge of processing cycles, we use the estimated mean and variance of processing cycles and exploit ECR to the fullest extent in the design of EPD. Simulation results show that EPD successfully guarantees the probabilistic deadlines while minimizing the energy consumption of mobile users, and can achieve significant improvement in energy saving when compared to a state-of-the-art approach.

The Age of Information (AoI) is a key metric to measure the freshness of information for IoT applications. Most of the existing analytical models for AoI are overly idealistic and do not capture state-of-the-art transmission technologies such as 5G as well as channel dynamics in both frequency and time domains. In this article, we present Kronos, a real-time 5G-compliant scheduler that minimizes AoI for IoT data collection. Kronos is designed to cope with highly dynamic channel conditions. Its main function is to perform RB allocation and to select the modulation and coding scheme for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we develop a GPU-based implementation of Kronos on commercial off-the-shelf Nvidia GPUs. Through extensive experimentation, we show that Kronos can find near-optimal solutions under submillisecond time scale. To the best of our knowledge, this is the first real-time AoI scheduler that is 5G compliant.

The spectrum access system (SAS) is being deployed as a key component of the emerging spectrum sharing paradigm to address the spectrum crunch facing the U.S. wireless industry. Ensuring security and privacy of this system against potential attacks is a task of paramount importance. In this article, we first introduce the SAS system, describing its three-tier access model, its functional architecture, and the spectrum management protocol. We then provide a comprehensive analysis of a variety of security and privacy attacks that an SAS is vulnerable to, and discuss their countermeasures. We identify key challenges, formalize threat models, and organize the discussion of SAS security into four categories: 1) SAS server security and privacy; 2) citizens broadband radio service device security; 3) security of environment sensing capability; and 4) communication protocol security. Finally, we suggest future research directions for spectrum management security.

Searchable symmetric encryption (SSE) has been widely applied in the encrypted database for queries in practice. Although SSE is powerful and feature-rich, it is always plagued by information leaks. Some recent attacks point out that forward privacy which disallows leakage from update operations, now becomes a basic requirement for any newly designed SSE schemes. However, the subsequent search operations can still leak a significant amount of information. To further strengthen security, we extend the definition of forward privacy and propose the notion of “forward search privacy”. Intuitively, it requires search operations over newly added documents do not leak any information about past queries. The enhanced security notion poses new challenges to the design of SSE. We address the challenges by developing the hidden pointer technique (HPT) and propose a new SSE scheme called Khons, which satisfies our security notion (with the original forward privacy notion) and is also efficient. We implemented Khons and our experiment results on large dataset (wikipedia) show that it is more efficient than existing SSE schemes with forward privacy.

In the recent decade, deep learning techniques have been widely adopted for founding artificial Intelligent applications, which led to successes in many data analysis tasks, such as risk assessment, medical predictions, and face recognition. Since the effectiveness of deep learning is directly proportional to the amount of data available, a large-scale collection of massive data is essential. Considering privacy and security concerns often prevent data owners from contributing sensitive data for training, researchers proposed several techniques to provide privacy guarantees of data in machine learning systems that contains multiple parties. However, all these works incurred frequent interactions between data owners during training, such that they came at a high communicational cost for data owners. To this end, in this article, we propose a new server-aid framework called non-interactive privacy-preserving multi-party machine learning (NPMML), which supports secure machine learning tasks without the participation of data owners. The NPMML framework significantly reduces data owners’ communicational overheads in multi-party machine learning. Moreover, we design a concrete construction for multi-layer neural networks based on NPMML. Finally, we evaluate the performance of NPMML by prototype implementation. The experimental result demonstrates that NPMML is communicational-efficient for data owners.

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation stays below a probability. In this article, we employ Chance-Constrained Programming (CCP) to exploit this idea of occasional interference threshold violation. We assume the uncertain channel gains are only known by their mean and covariance. These quantities are slow-changing and easy to estimate. Our main contribution is to introduce a novel and powerful mathematical tool called Exact Conic Reformulation (ECR), which reformulates the intractable chance constraints into tractable convex constraints. Further, ECR guarantees an equivalent reformulation from linear chance constraints into deterministic conic constraints without the limitations associated with Bernstein Approximation, on which our research community has been fixated on for years. Through extensive simulations, we show that our proposed solution offers a significant improvement over existing approaches in terms of performance and ability to handle channel correlations (where Bernstein Approximation is no longer applicable).

Automotive communication networks, represented by the CAN bus, are acclaimed for enabling real-time communication between vehicular ECUs but also criticized for their lack of effective security mechanisms. Various attacks have demonstrated that this security deficit renders a vehicle vulnerable to adversarial control that jeopardizes passenger safety. A recent standardization effort led by AUTOSAR has provided general guidelines for developing next-generation automotive communication technologies with built-in security mechanisms. A key security mechanism is message authentication between ECUs for countering message spoofing and replay attack. While many message authentication schemes have been proposed by previous work, the important issue of session key establishment with AUTOSAR compliance was not well addressed. In this paper, we fill this gap by proposing an AUTOSAR-compliant key management architecture that takes into account practical requirements imposed by the automotive environment. Based on this architecture, we describe a baseline session key distribution protocol called SKDC that realizes all designed security functionalities, and propose a novel secret-sharing-based protocol called SSKT that yields improved communication efficiency. Both SKDC and SSKT are customized for CAN/CAN-FD bus deployment. We implemented the two protocols on commercial microcontroller boards and evaluated their performance with hardware experiment and extrapolation analysis. The result shows while both protocols are performant, SSKT achieves superior computation and communication efficiency at scale.

Big data analytics is having a profound impact on many sectors of the economy by transforming raw data into actionable intelligence. However, increased use of sensitive business and private personal data with no or limited privacy safeguards has raised great concerns among individuals and government regulators. To address the growing tension between the need for data utility and the demand for data privacy, trusted execution environment (TEE) is being used in academic research as well as industrial application as a powerful primitive to enable confidential computation on the private data with only the result disclosed but not the original private data. While much of the current research has been focusing on protecting the TEE against attacks (e.g. side-channel information leakage), the security and privacy of the applications executing inside a TEE enclave has received little attention. The general attitude is that the application is running inside a trusted computing base (TCB), and therefore can be trusted. This assumption may not be valid when it comes to unverified third-party applications. In this paper, we present PrivacyScope, a static code analyzer designed to detect leakage of private data by an application code running in a TEE. PrivacyScope accomplishes this by analyzing the application code and identifying violations of a property called nonreversibility. We introduce nonreversibility since the classical noninterference property falls short of detecting private data leakage in certain scenarios, e.g., in machine learning (ML) programs where the program output is always related to (private) input data. Given its strict reliance on observable state, the noninterference falls short of detecting private data leakage in these situations. By design, PrivacyScope detects both explicit and implicit information leakage. The nonreversibility property is formally defined based on the noninterference property. Additionally, we describe the algorithms for PrivacyScope as extensions to the runtime semantics of a general language. To evaluate the efficacy of our approach and proof-of-feasibility prototype, we apply PrivacyScope to detect data leakage in select open-source ML code modules including linear regression, k-means clustering and collaborative filtering. Also, PrivacyScope can detect intentional data leakage code injected by a programmer. We responsibly disclosed all the discovered vulnerabilities leading to disclosure of private data in the open-source ML program we analyzed.

Hybrid beamforming (HB) architecture has been widely recognized as the most promising solution to mmWave MIMO systems. A major practical challenge for HB is to obtain a solution in ~1 ms, which is an extremely stringent but necessary time requirement for its deployment in the field. In this paper, we present the design and implementation of Turbo-HB, codename for a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 1 ms. The key ideas in Turbo-HB include (i) reducing the complexity of SVD techniques by exploiting the limited number of channel paths at mmWave frequencies, and (ii) designing and implementing a parallelizable algorithm for a large number of matrix transformations. We validate Turbo-HB by implementing it on an off-the-shelf Nvidia GPU. Through extensive experiments, we show that Turbo-HB can meet ~1 ms timing requirement while delivering competitive throughput performance compared to state-of-the-art algorithms.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when each source node has an AoI requirement (which we call Maximum AoI Threshold (MAT)). Specifically, we want to determine whether or not a vector of MATs for the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure called Cyclic Scheduler Detection (CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under ln 2. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.

This article investigates the dynamic multiplexing of enhanced mobile broadband (eMBB) and ultrareliable and low latency communications (URLLC) on the same channel in 5G NR. Due to significant difference in transmission time scale, URLLC employs a preemptive puncturing technique to multiplex its traffic onto eMBB traffic for transmission. The optimization problem to solve is to minimize the adverse impact of such preemptive puncturing on eMBB users. We present DEMUX - a model-free deep reinforcement learning (DRL)-based solution to this problem. The essence of DEMUX is to use deep function approximators (neural networks) to learn an optimal algorithm for determining the preemption solution in each eMBB transmission time interval (TTI). Our novel contributions in the design of DEMUX include the first use of the DRL method with a large and continuous action domain for resource scheduling in NR, a mechanism to ensure fast and stable learning convergence by exploiting the intrinsic properties of the problem, and a mechanism to obtain a feasible preemption solution from the unconstrained output of a neural network while minimizing loss of information. The experimental results show that DEMUX significantly outperforms state-of-the-art algorithms proposed in the 3GPP standards body and the literature.

Non-orthogonal multiple access (NOMA) has emerged as a new multiple access paradigm for wireless networks. Although many results have been produced for NOMA, most of them are limited to theoretical exploration and performance analysis in cellular networks. Very limited progress has been made so far in the design of practical NOMA schemes for wireless local area networks (WLANs). In this paper, we propose a practical downlink NOMA scheme for WLANs and evaluate its performance in real-world wireless environments. Our NOMA scheme has three key components: precoder design, user grouping, and successive interference cancellation (SIC). On the transmitter side, we first formulate the precoding design problem as an optimization problem and then devise an efficient algorithm to construct precoders for downlink NOMA transmissions. We further propose a lightweight user grouping algorithm to ensure the success of SIC at the receivers. On the receiver side, we propose a new SIC method to decode the desired signal in the presence of strong interference. In contrast to existing SIC methods, our SIC method does not require channel estimation to decode the signals, thereby improving its resilience to interference. We have built a prototype of the proposed NOMA scheme on a wireless testbed. Experimental results show that, compared to orthogonal multiple access (OMA), the proposed NOMA scheme can significantly improve the weak user's date rate (93% on average) and considerably improve WLAN's weighted sum rate (36% on average).

Degree-of-freedom (DoF) based models have become prevalent in studying MIMO-based wireless networks. However, most existing DoF-based models assume the channel matrix is of full-rank. Such a simplifying assumption has gradually become problematic, particularly when the number of antennas increases and the propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for the DoF-based model under general channel rank conditions. We start with a fundamental understanding on how MIMO's DoFs are consumed at each node for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank-deficient channels. Based on this understanding, we develop a DoF model that can be used for identifying the DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks under general channel rank conditions. Specifically, we find that for IC, shared DoF consumption at both transmit and receive nodes is critical for efficient DoF allocation. Further, we show that DoF consumption under the existing full-rank assumption is a special case of our generalized DoF model. Based on case studies, we show that the general IC model can achieve larger feasible DoF regions or improved objective values than existing unilateral IC models. The findings of this paper pave the way for future research of many-antenna networks under general channel rank conditions.

Edge computing (EC) is an emerging paradigm to push sufficient computation resources towards the network edge, improving application performance significantly by offloading applications to the edge computing node. We investigate continuous application offloading decision in EC, for which it is uncertain how users operate continuous applications and how long continuous applications last before completion. That means some characteristics of continuous applications, e.g., the number of user operations, the uploading and downloading data size for offloading computation of each user operation, and the number of central processing unit (CPU) cycles required to execute computation of each user operation, are unknown when making offloading decision. In this scenario, an energy consumption constrained average response time minimization problem among multiple users for continuous applications under uncertainty is formulated. To tackle this problem, we propose the Response Time-Improved Offloading algorithm with Energy Constraint (RTIOEC) to make offloading decision with fewer characteristics of applications. The evaluation results show that the RTIOEC algorithm achieves comparatively short average response time of continuous applications while satisfying the energy consumption constraint with a predefined upper bound of violation probability. Our results demonstrate the practicality of the RTIOEC algorithm in offloading decision in EC for continuous applications under uncertainty.

Underlay coexistence is an effective mechanism to improve spectrum effïciency by having picocells coexist with macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocell, it is impossible to have complete knowledge of channel gains between them. Under such circumstance, chance-constrained programming (CCP) is shown to be the ideal optimization tool to address such uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G's timing requirement. To address this problem, we propose a novel scheduler called GUC (stands for GPU-based Underlay Coexistence) to find an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small problems that are suitable for parallel computation on GPU platforms. Through extensive experiments, we show that GUC reduces the scheduling computation time by at least 10,000 times comparing to commercial solvers (on CPU) while achieving an average of 90% optimality.

Age of information (AoI) is a powerful new metric to quantify the freshness of information and has gained increasing popularity in IoT applications. Existing models on AoI remain primitive and do not consider state-of-the-art transmission technologies such as 5G. They also fail to consider the impact of dynamic channel conditions. In this paper, we present Kronos, a 5G-compliant AoI scheduling algorithm that can cope with highly dynamic channel conditions. Kronos is capable of performing RB allocation and selecting MCS for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we propose a GPU-based implementation of Kronos on low-cost offthe-shelf GPUs. Through simulations and experiments, we show that Kronos can find near-optimal AoI scheduling solutions in sub-millisecond time scale. To the best of our knowledge, this is the first 5G-compliant real-time AoI scheduler that can cope with dynamic channel conditions.

In underlay coexistence, secondary users (SUs) attempt to keep their interference to the primary users (PUs) under a threshold. Due to the absence of cooperation from the PUs, there exists much uncertainty at the SUs in terms of channel state information (CSI). An effective approach to cope such uncertainty is to introduce occasional interference threshold violation by the SUs, as long as such occasional violation can be tolerated by the PUs. This paper exploits this idea through a chance constrained programming (CCP) formulation, where the knowledge of uncertain CSI is limited to only the first and second order statistics rather than its complete distribution information. Our main contribution is the introduction of a novel and powerful technique, called Exact Conic Reformulation (ECR), to reformulate the intractable chance constraints. ECR guarantees an equivalent reformulation for linear chance constraints into deterministic conic constraints and does not suffer from the limitations associated with the state-of-the-art approach -- Bernstein Approximation. Simulation results confirm that ECR offers significant performance improvement over Bernstein Approximation in uncorrelated channels and a competitive solution in correlated channels (where Bernstein Approximation is no longer applicable).

As an important application of the Internet of Things, many remote monitoring systems adopt a device-to-cloud network paradigm. In a remote patient monitoring case, various resource-constrained devices are used to measure the health conditions of a target patient in a distant non-clinical environment and the collected data are sent to the cloud backend of an authorized health care service for processing and decision making. As the measurements involve private patient information, access control and trustworthy processing of the confidential data become very important. Software-based solutions that adopt advanced cryptographic tools, such as attribute-based encryption and fully homomorphic encryption, can address the problem, but they also impose substantial computation overhead on both client and server sides. In this paper, we deviate from the conventional software-based solutions and propose a secure and efficient remote monitoring framework, called SRM, using the latest hardware-based trustworthy computing technology, such as Intel SGX. In addition, we present a robust and lightweight “heartbeat” protocol to handle notoriously difficult key revocation problem. We implemented a prototype of the framework for SRM and show that SRM can protect user data privacy against unauthorized parties, with minimum performance cost compared to existing software-based solutions.

Duplicated data storage wastes memory resources and brings extra data-management load and cost to cloud service providers (CSPs). Various feasible schemes to deduplicate encrypted cloud data have been reported. However, their successful deployment in practice depends on whether all system players or stakeholders are willing to accept and execute them in a cooperative way, which was scarcely investigated in the previous literature. In this paper, we employ a noncooperative game to model the interactions in a client-side server-controlled deduplication scheme (S-DEDU) and construct an incentive mechanism based on payment discount to motivate its final acceptance. The experimental results based on a real-world dataset demonstrate the individual rationality, incentive compatibility, profitability, and robustness of our incentive mechanism.

With the emergence of Internet of Things, mobile devices are generating more network traffic than ever. TrustZone is a hardware-enabled trusted execution environment for ARM processors. While TrustZone is effective in providing the much-needed memory isolation, we observe that it is possible to derive secret information from secure world using the cache contention, due to its high-performance cache sharing design. In this work, we propose TruSense to study the timing-based cache side-channel information leakage of TrustZone. TruSense can be launched from not only the normal world operating system but also a non-privileged user application. Without access to virtual-to-physical address mapping in user applications, we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate how a normal world attacker can steal fine-grained secret in the secure world. We also discuss possible mitigations for the information leakage.

In recent years, degree-of-freedom (DoF) based models were proven to be very successful in studying MIMO-based wireless networks. However, most of these studies assume channel matrix is of full-rank. Such assumption, although attractive, quickly becomes problematic as the number of antennas increases and propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for DoF-based model under rank-deficient conditions. We start with a fundamental understanding on how MIMO's DoFs are consumed for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank deficiency. Based on this understanding, we develop a general DoF model that can be used for identifying DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks. Specifically, we found that shared DoF consumption at transmit and receive nodes is critical for optimal allocation of DoF for IC. The results of this paper serve as an important tool for future research of many-antenna based MIMO networks.

Search over encrypted data (SE) enables a client to delegate his search task to a third-party server that hosts a collection of encrypted documents while still guaranteeing some measure of query privacy. Software-based solutions using diverse cryptographic primitives have been extensively explored, leading to a rich set of secure search indexes and algorithm designs. However, each scheme can only implement a small subset of information retrieval (IR) functions and often with considerable search information leaked. Recently, the hardware-based secure execution has emerged as an effective mechanism to securely execute programs in an untrusted software environment. In this paper, we exploit the hardware-based execution environment (TEE) and explore a software and hardware combined approach to address the challenging secure search problem. For functionality, our design can support the same spectrum of plaintext IR functions. For security, we present oblivious keyword search techniques to mitigate the index search trace leakage. We build a prototype of the system using Intel SGX. We demonstrate that the proposed system provides broad support of a variety of search functions and achieves computation efficiency comparable to plaintext data search with elevated security protection.

5G New Radio (NR) is designed to operate under a broad range of frequency bands and support new applications with ultra-low latency. To support its diverse operating conditions, a set of different OFDM numerologies has been defined in the standards body. Under this numerology, it is necessary to perform scheduling with a time resolution of ∼100 μs. This requirement poses a new challenge that does not exist in LTE and cannot be supported by any existing LTE schedulers. In this paper, we present the design of GPF -- a GPU-based proportional fair (PF) scheduler that can meet the ∼100 μs time requirement. The key ideas include decomposing the scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF on an off-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 $\mathrmμs time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in NR.

WiFi-based motion sensing has received a lot of research attention in recent years. Taking advantage of Channel State Information(CSI) collected from physical layer, previous techniques are able to extract useful information from CSI values to infer human movements. However, these works concentrate either on coarse-grained motion sensing or on fine-grained but context-related motion sensing. In this paper, we propose WiTalk, a new fine-grained human motion sensing technique with the distinct context-free character. To profile human motion using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. We verify the proposed technique in the application scenario of lip reading, where the fine-grained motion is the mouth movements. We implement WiTalk on a commercial laptop. Experiment results show that WiTalk can achieve over 92.3% recognition accuracy to discern a set of 12 syllables and 74.3% accuracy to discern a set of short sentences up to six words.

Interference neutralization (IN) is regarded as a promising interference management techniques for multi-hop wireless networks. Yet most existing results of IN are limited to two-hop networks such as the relay-aided cellular network. Little progress has been made so far in the exploration of IN in generic multi-hop (more than two hops) networks. This paper aims to bridge this gap by developing an optimization framework for IN in a generic multi-hop network with the objective of maximizing the end-to-end throughput of multiple coexisting communication sessions. We first derive a mathematical model for IN in a special one-hop network to characterize the capability of IN, and then generalize this model to a multi-hop network. Based on the IN model, we develop a cross-layer optimization framework for a multi-hop network with the objective of fully translating the benefits of IN to the end-to-end throughput of the multi-hop sessions. To evaluate the performance of IN in multi-hop networks, we compare its performance against the case where IN is not employed. Simulation results show that the use of IN can significantly (more than 50%) increase the session throughput and, more notably, the throughput gain of IN increases with the node density and traffic intensity in the network.

With increasingly complex cyber attacks occurring every day, memory-based forensic techniques are becoming instrumental in digital investigations. Forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. However, the foundation of this analysis can be invalidated if the memory acquisition has been altered. In this paper, we study the feasibility of malicious software misusing architectural features to sabotage memory forensics. The misuse of two architectural features, namely, physical address layout and secure containers, is presented. The first architectural feature explored in this paper is the physical address layout. It is used by the northbridge to route memory access to either physical memory or I/O devices on x86 platforms. Observing this design choice, we propose Hidden in I/O Space (HIveS), which manipulates CPU registers to alter the physical address layout to conceal memory. The system uses a novel I/O shadowing technique to lock a memory region named HIveS memory into I/O address space to prevent access. Two novel techniques, blackbox write and TLB camouflage, are developed to further protect the unlocked HIveS memory against memory forensics while allowing access for attackers. The second architectural feature explored in this paper is hardware-aided secure execution technology. More specifically, hardware-enforced memory encryption in Intel secure guard extension is used in malicious enclave software (Malclaveware) to prevent introspection and memory forensics. A prototype of HIveS is built and tested against a set of memory acquisition tools for both Windows and Linux running on the x86 platform. Malclaveware is also prototyped in Windows to demonstrate the risk. More importantly, we proposed countermeasures and mitigations for the newly discovered attacks. Through these discussions, we aim to raise the awareness of the potential risks of misusing hardware architectural features.

Cooperation between primary and secondary networks offers significant benefits in data forwarding. But, cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other's traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an ε-approximation curve and prove its error bounds in both cost dimensions. Based on the ε-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original ε-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the ε-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other's traffic cooperatively.

Unutilized spectrum, i.e. spectrum holes, are opportunities that may be used for communication or other RF services. In this paper, we explore adversarial attacks that reduce the size of spectrum holes by showing their advantage compared to a random jammer. Using a game-theoretical approach, we design an optimal scanning strategy that provides an increased probability of detecting such an attack. The advantage of our strategy is achieved by focusing scanning efforts on bands that are more likely to be attacked, and neglecting the others. However, such focused scanning is a disadvantage since, if the adversary has a different objective, he can safely sneak usage of the bands neglected by such a specially-tuned spectrum scanner. To deal with this problem, we also derive the optimal scanning allocation that balances between applying the anti-spectrum holes attack scanning strategy and scanning the neglected bands so as to prevent the possibility of the adversary using those bands without being detected.

As computing system continues to play an increasing role in daily life, user authentication is now an important component. One of the most widely accepted methods for user authentication is through proof of knowledge of a piece of secret information, such as password. However, entering this non-mutable secret for authentication in public space often allows attackers to steal the secret by shoulder surfing or video recording. We observe that it is possible to block attacker's access to user input using augmented reality (AR) display, which is only available to the user. Based on this intuition, we present AugAuth, an authentication scheme in AR using commercial off-the-shelf(COTS) gesture control sensors as an input device. AugAuth can resist against shoulder surfing by presenting user input interface that is only visible to the user and is unique every time. To enable user input with finger movement using the gesture control armband, Myo, we have solved several challenges in electromyogram signal processing, such as annotating the start of signal and finger classification. The experiment results for our input system of a group of volunteers show that our finger classification function has high accuracy and AugAuth is practical for use in real life authentication scenarios.

In this paper, we investigate the problem of integrity auditing for cloud deduplication storage. Specifically, in addition to the outsourced data confidentiality, we also aim to ensure the integrity of the deduplicated cloud storage. With the existing works based on Provable Data Possession (PDP)/Proof of Retrievability (PoR), we are either required to rely on a fully trusted proxy server or inevitably sacrifice the privacy and efficiency. In contrast, we present a novel message-locked integrity auditing scheme without an additional proxy server, which is applicable to both file-level and chunk-level deduplication systems. In particular, our scheme is storage efficient in the sense that apart from eliminating the ciphertext redundancy, we also enable the integrity tag deduplication by a message-derived signing key, which merely incurs minimal client-side computation overhead. Besides, we can still publicly perform the integrity check over any client's cloud storage by incorporating the proxy re-signature technique. We show that the proposed scheme will not disclose the data ownership information and is provably secure under the Computational Diffie-Hellman (CDH) assumption in the random oracle model. Finally, the performance evaluation demonstrates its effectiveness and efficiency.

As the cost of human full genome sequencing continues to fall, we will soon witness a prodigious amount of human genomic data in the public cloud. To protect the confidentiality of the genetic information of individuals, the data has to be encrypted at rest. On the other hand, encryption severely hinders the use of this valuable information, such as Genome-wide Range Query (GRQ), in medical/genomic research. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from practical deployment in terms of efficiency and scalability due to the data volume in human genome sequencing. In this paper, we investigate the problem of secure GRQ over human raw aligned genomic data in a third-party outsourcing model. Our solution contains a novel secure range query scheme based on multi-keyword symmetric searchable encryption (MSSE). The proposed scheme incurs minimal ciphertext expansion and computation overhead. We also present a hierarchical GRQ-oriented secure index structure tailored for efficient and large-scale genomic data lookup in the cloud while preserving the query privacy. Our experiment on real human genomic data shows that a secure GRQ request with range size 100,000 over more than 300 million encrypted short reads takes less than 3 minutes, which is orders of magnitude faster than existing solutions.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.

Millimeter-wave communication in 60 GHz band is considered a promising technology to meet the explosive growth of data demand in Wi-Fi based WLAN. To address potential blockage for 60 GHz signals, multiple APs are proposed for such WLAN. This paper addresses the important problem of AP assignment and transmission scheduling for a multi-AP 60 GHz WLAN. We propose two AP assignment schemes with different complexity and study how to maximize user throughput with joint consideration of AP assignment and transmission scheduling. We advocate to use one-shot AP assignment-based scheduling due to its simplicity for implementation. To address real-time online traffic and human blockage, we propose an online algorithm to implement the one-shot AP assignment scheme without altering the AP assignment for other existing users. Through performance evaluation, we show that the proposed online algorithm is competitive when compared to the offline algorithm.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

Underwater acoustic (UWA) networks are a key form of communications for human exploration and activities in the oceanographic space of the earth. A fundamental issue of UWA communications is large propagation delays due to water medium, which has posed a grand challenge in UWA network protocol design. Conventional wisdom of addressing this issue is to live with this disadvantage by inserting a guard interval to introduce immunity to propagation delays. Recent advances in interference alignment (IA) open up a new direction to address this issue and promise a great potential to improve network throughput by exploiting large propagation delays. In this paper, we investigate propagation delay-based IA (PD-IA) in multi-hop UWA networks. We first develop a set of simple constraints to characterize PD-IA feasible region at the physical layer. Based on the set of PD-IA constraints, we develop a distributed PD-IA scheduling algorithm to greedily maximize interference overlapping possibilities in a multi-hop UWA network. Simulation results show that the proposed PD-IA algorithm yields higher throughput than an idealized benchmark algorithm without propagation delays, indicating that large propagation delays are not adversarial but beneficial for network throughput performance.

Interference alignment (IA) is widely regarded as a promising interference management technique in wireless networks. Despite its rapid advances in cellular networks, most results of IA are limited to information-theoretic exploration or physical-layer signal design. Little progress has been made so far to advance IA in cellular networks from a networking perspective. In this paper, we aim to fill this gap by studying IA in large-scale cellular networks. For the uplink, we propose an OFDM-based IA scheme and prove its feasibility at the physical layer by showing that all data streams in the IA scheme can be transported free of interference. Based on the IA scheme, we develop a cross-layer IA optimization framework that can fully translate the benefits of IA to throughput gain in cellular networks. Furthermore, we show that the IA optimization problem in the downlink can be solved in the exactly same way as that in the uplink. Simulation results show that our OFDM-based IA scheme can significantly increase the user throughput and the throughput gain increases with user density in the network.

A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users' private location information during a proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain an advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) a location based handshake that establishes secure communications among strangers, who do not have a pre-shared secret, and (2) a privacy-preserving proximity test without revealing the user's actual location to the server or other users not within the proximity. The proposed scheme is based on a novel concept, i.e., spatial-temporal location tags, and we put forward a location tag construction method using environmental signals that provides an unforgeable location proof. We use Bloom filters to efficiently represent users' location tags and vicinity regions. We exploit fuzzy extractor, a lightweight cryptographic primitive, to extract shared secrets between matching location tags. We conduct extensive analysis, simulation, and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.

Cross-client data deduplication has been widely used to eliminate redundant storage overhead in cloud storage system. Recently, Abadi et al. introduced the primitive of MLE2 with nice security properties for secure and efficient data deduplication. However, besides the computationally expensive noninteractive zero-knowledge proofs, their fully randomized scheme (R-MLE2) requires the inefficient equality-testing algorithm to identify all duplicate ciphertexts. Thus, an interesting challenging problem is how to reduce the overhead of R-MLE2 and propose an efficient construction for R-MLE2. In this paper, we introduce a new primitive called μR-MLE2, which gives a partial positive answer for this challenging problem. We propose two schemes: static scheme and dynamic scheme, where the latter one allows tree adjustment by increasing some computation cost. Our main trick is to use the interactive protocol based on static or dynamic decision trees. The advantage gained from it is, by interacting with clients, the server will reduce the time complexity of deduplication equality test from linear time to efficient logarithmic time over the whole data items in the database. The security analysis and the performance evaluation show that our schemes are Path-PRV-CDA2 secure and achieve several orders of magnitude higher performance for data equality test than R-MLE2 scheme when the number of data items is relatively large.

Among all types of computations, the polynomial function evaluation is a fundamental, yet an important one due to its wide usage in the engineering and scientific problems. In this paper, we investigate publicly verifiable outsourced computation for polynomial evaluation with the support of multiple data sources. Our proposed verification scheme is universally applicable to all types of polynomial computations and allows the clients to outsource new data at any time. While the existing solutions only support the verification for polynomial evaluation over a single data source, i.e., all the inputs of the polynomial function are outsourced and signed by a single entity, our solution supports polynomial evaluations over multiple different data sources, which are more common and have wider applications, e.g., to assess the city air pollution, one needs to evaluate the environmental data uploaded from the multiple environmental monitor sites. In our proposed scheme, the verification cost for the client is independent with either the input size or the polynomial size so that it scales well in practice. We formally prove the correctness and soundness of our scheme and conduct numerical analysis and evaluation study to validate its high efficiency and scalability. The experimental results show that the data contributor signing 1000 new data only takes 2.1 s, and the verification of the delegated polynomial function takes only 22 ms, which is practically efficient for the real-world applications.

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%.

There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed 2x of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network.

Existing spectrum sharing paradigms have set clear boundaries between the primary and secondary networks. There is either no or very limited node-level cooperation between the primary and secondary networks. In this paper, we develop a new and bold spectrum-sharing paradigm beyond the state of the art for future wireless networks. We explore network cooperation as a new dimension for spectrum sharing between the primary and secondary users. Such network cooperation can be defined as a set of policies under which different degrees of cooperation are to be achieved. The benefits of this paradigm are numerous, as they allow integrating resources from two networks. There are many possible node-level cooperation policies that one can employ under this paradigm. For the purpose of performance study, we consider a specific policy called United cooperation of Primary and Secondary (UPS) networks. UPS allows a complete cooperation between the primary and secondary networks at the node level to relay each other's traffic. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. We also develop an approximation solution based on a piece-wise linearization technique. Simulation results show that UPS offers significantly better throughput performance than that under the interweave paradigm.

Uploading data streams to a resource-rich cloud server for inner product evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties' different keys. However, the present solutions either depend on a single key assumption or powerful yet practically-inefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple data sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.

With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. LeveragingTrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.

In this paper, we present MobTrack, a single device system which aims to locate interfering radios on unlicensed ISM band in indoor environments. Compared with existing techniques which require a deployment of dense access points (APs), MobTrack only demands a single device equipped with multiple antennas. The location of an interfering signal source are estimated by computing the angle of arrival (AoA) of Line of Sight (LoS) component using an antenna array. Taking advantage of cyclostationary property, MobTrack differentiates interfering signals from working signals. By moving the device around for a short distance within one meter, it depresses multipath effects and determines the LoS component. Simultaneously, the AoAs on the moving trace are recorded to estimate the location of the interfering radio by triangulation. We evaluate the performance of MobTrack by setting up a prototype experimental system. Compared with recent interference localization schemes, MobTrack has much lower hardware complexity and gets better localization accuracy with a median of 0.55 meters.

Interference neutralization (IN) is an interference management technique that allows simultaneous transmission of multiple links by nullifying their mutual interference in the air via cooperation among the transmitters. Although IN has been studied from information theoretic perspective, its potential for a general multi-hop wireless network has not been explored. The goal of this paper is to understand IN in a multi-hop wireless network from networking perspective. We first establish an IN reference model. Based on this reference model, we develop a set of feasibility constraints for a subset of links to be active simultaneously. By identifying each eligible neutralization node (called neut), we study IN in a general multi-hop network and develop a set of necessary constraints to characterize neut selection, IN, and scheduling. These constraints allow us to study the performance of multi-hop networks without the need of getting involved into onerous signal design issues at the physical layer. Finally, we apply our IN model and constraints to study a throughput maximization problem and show that the use of IN can generally increase network throughput. In particular, throughput gain is most significant when the node density increases.

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1.

Physical layer security for wireless communication is broadly considered as a promising approach to protect data confidentiality against eavesdroppers. However, despite its ample theoretical foundation, the transition to practical implementations of physical-layer security still lacks success. A close inspection of proven vulnerable physical-layer security designs reveals that the flaws are usually overlooked when the scheme is only evaluated against an inferior, single-antenna eavesdropper. Meanwhile, the attacks exposing vulnerabilities often lack theoretical justification. To reduce the gap between theory and practice, we posit that a physical-layer security scheme must be studied under multiple adversarial models to fully grasp its security strength. In this regard, we evaluate a specific physical-layer security scheme, i.e. orthogonal blinding, under multiple eavesdropper settings. We further propose a practical "ciphertext-only attack" that allows eavesdroppers to recover the original message by exploiting the low entropy fields in wireless packets. By means of simulation, we are able to reduce the symbol error rate at an eavesdropper below 1% using only the eavesdropper's receiving data and a general knowledge about the format of the wireless packets.

Cooperation has become an essential element in spectrum sharing between the primary and secondary networks. A new trend in cooperation is to allow the primary and secondary networks to cooperate on the node level for data forwarding. This new paradigm allows to pool network resources from both the primary and secondary networks and allows users in each network to access a much richer network infrastructure in a combined network. This paper offers an in-depth study of such node-level cooperation by explaining its optimal throughput curve—the maximum achievable throughput for both the primary and secondary users. We formulate the problem as a multicriteria optimization problem with the goal of maximizing the throughput of both the primary and secondary users. Through a novel approach based on weighted Chebyshev norm, we transform the multicriteria optimization problem into a single criteria optimization problem and find a sequence of Pareto-optimal points iteratively. Based on the Pareto-optimal points, we construct the throughput curve and show that it provides an $\varepsilon $ -approximation to the optimal curve. We prove some important properties of the optimal throughput curve. Through a case study, we show that the throughput region (the area under the throughput curve) under node-level cooperation is substantially larger than that when there is no node-level cooperation.

The notion of verifiable database (VDB) enables a resource-constrained client to securely outsource a very large database to an untrusted server so that it could later retrieve a database record and update a record by assigning a new value. Also, any attempt by the server to tamper with the data will be detected by the client. When the database undergoes frequent while small modifications, the client must re-compute and update the encrypted version (ciphertext) on the server at all times. For very large data, it is extremely expensive for the resources-constrained client to perform both operations from scratch. In this paper, we formalize the notion of verifiable database with incremental updates (Inc-VDB). Besides, we propose a general Inc-VDB framework by incorporating the primitive of vector commitment and the encrypt-then-incremental MAC mode of encryption. We also present a concrete Inc-VDB scheme based on the computational Diffie-Hellman (CDH) assumption. Furthermore, we prove that our construction can achieve the desired security properties.

Jamming attack is a serious threat to the wireless communications. Reactive jamming maximizes the attack efficiency by jamming only when the targets are communicating, which can be readily implemented using software-defined radios. In this paper, we explore the use of the multi-input multi-output (MIMO) technology to achieve jamming resilient orthogonal frequency-division multiplexing (OFDM) communication. In particular, MIMO interference cancellation treats jamming signals as noise and strategically cancels them out, while transmit precoding adjusts the signal directions to optimize the decoding performance. We first investigate the reactive jamming strategies and their impacts on the MIMO-OFDM receivers. We then present a MIMO-based anti-jamming scheme that exploits MIMO interference cancellation and transmit precoding technologies to turn a jammed non-connectivity scenario into an operational network. We implement our jamming resilient communication scheme using software-defined radios. Our testbed evaluation shows the destructive power of reactive jamming attack, and also validates the efficacy and efficiency of our defense mechanisms in the presence of numerous types of reactive jammers with different jamming signal powers.

Interference alignment (IA) is a powerful technique to handle interference in wireless networks. Since its inception, IA has become a central research theme in the wireless communications community. Due to its intrinsic nature of being a physical layer technique, IA has been mainly studied for point-to-point or single-hop scenario. There is a lack of research of IA from a networking perspective in the context of multi-hop wireless networks. The goal of this paper is to make such an advance by bringing IA technique to multi-hop MIMO networks. We develop an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine IA for a subset of interfering streams. We further prove the feasibility of this IA model by showing that a DoF vector can be supported free of interference at the physical layer as long as it satisfies the constraints in our IA model. Based on the proposed IA model, we develop an IA design space for a multi-hop MIMO network. To study how IA performs in a multi-hop MIMO network, we compare the performance of a network throughput optimization problem based on our developed IA design space against the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

Recently, a new MIMO degree-of-freedom (DoF) model was proposed to allocate DoF resources for spatial multiplexing (SM) and interference cancellation (IC) in a multi-hop network. Although this DoF model promises many benefits, it hinges upon a global node ordering to keep track of IC responsibilities among all the nodes. An open question about this model is whether its global ordering property can be achieved among the nodes in the network through distributed operations. In this paper, we explore this question by studying DoF scheduling in a multi-hop MIMO network, with the objective of maximizing the minimum throughput among a set of sessions. We propose an efficient DoF scheduling algorithm to solve it and show that our algorithm only requires local operations. We prove that the resulting DoF scheduling solution is globally feasible and show that there exists a corresponding feasible global node ordering for IC, albeit such global ordering is implicit. Simulation results show that the solution values obtained by our algorithm are relatively close to the upper bound values computed by CPLEX solver, thereby indicating that our algorithm is highly competitive.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.

This paper studies the coexistence of heterogeneous multi-hop networks, which use different physical-layer technologies. We propose a new paradigm, called cooperative interference mitigation (CIM), which exploits recent advancement in interference cancellation (IC), such as technology-independent multiple output. CIM makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone's performance. We first show the feasibility of CIM among heterogeneous multi-hop networks by exploiting only channel-ratio information. Then, we establish two tractable models to characterize the CIM behaviors of both networks by using full IC and receiver-side IC only. We propose two bi-criteria optimization problems aiming at maximizing both networks' throughput, while cooperatively canceling the interference between them based on our two models. Several simulations are carried out to compare the Pareto-optimal throughput curves by using our CIM paradigms and traditional interference-avoidance (IAV) paradigm. By comparing the results from CIM and IAV, we show that CIM could remarkably improve the coexisting networks' throughput in different network settings.

The classical approach to interference management in wireless medium access is based on avoidance. Recently, there is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. This was made possible by a number of advances at the physical layer. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we aim to close this gap by offering a systematic study of SIC in a multi-hop wireless network. After gaining a fundamental understanding of SIC's capability and limitation, we propose a cross-layer optimization framework for SIC that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC behaves in a multi-hop wireless network.

Recently, degree-of-freedom (DoF)-based models have been widely used to study MIMO network performance. Existing DoF-based models differ in their interference cancellation (IC) behavior and many of them suffer from either loss of solution space or possible infeasible solutions. To overcome these limitations, a new DoF-based model, which employs an IC scheme based on node-ordering was proposed. In this paper, we apply this new DoF IC model to study a throughput maximization problem in a multihop MIMO network. The problem formulation involves joint consideration of flow routing and DoF allocation and falls in the form of a mixed-integer linear program (MILP). Our main contribution is an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The algorithm employs a sequential fixing framework to obtain an initial feasible solution and then improves the solution by exploiting: 1) the impact of node ordering on DoF consumption for IC at a node and 2) route diversity in the network. Simulation results show that the solutions obtained by our proposed algorithm are competitive and feasible.

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.

With the growing complexity of computing systems, memory based forensic techniques are becoming instrumental in digital investigations. Digital forensic examiners can unravel what happened on a system by acquiring and inspecting in-memory data. Meanwhile, attackers have developed numerous anti-forensic mechanisms to defeat existing memory forensic techniques by manipulation of system software such as OS kernel. To counter anti-forensic techniques, some recent researches suggest that memory acquisition process can be trusted if the acquisition module has not been tampered with and all the operations are performed without relying on any untrusted software including the operating system.

Cyclostationary analysis is a powerful tool to study the Signal-Selective Direction Estimation (SSDE) problem as different types of wireless signals have different cyclostationary patterns. Generally speaking, each type of wireless signal has unique cyclic frequencies with the frequency-selective property, which distinguishes itself from other types of signals. The cyclostationary property of a signal may be induced by its modulation method, its carrier frequency, and/or its frame structure. In this paper, we study the cyclostationary property of IEEE 802.11 (WiFi) signals induced by their underlying OFDM frame structure, which includes pilots, cyclic prefix (CP), and preambles. We first analyze the pilot-induced, CP-induced, and preamble-induced cyclostationary properties of WiFi signals, respectively. We then derive their spectral correlation function (SCF) and investigate their applicability to solving the SSDE problem. Simulation results show that the pilot-induced cyclostationary property of WiFi signals is a promising feature that can be used to solve the SSDE problem.

Encrypted data search allows cloud to offer fundamental information retrieval service to its users in a privacy-preserving way. In most existing schemes, search result is returned by a semi-trusted server and usually considered authentic. However, in practice, the server may malfunction or even be malicious itself. Therefore, users need a result verification mechanism to detect the potential misbehavior in this computation outsourcing model and rebuild their confidence in the whole search process. On the other hand, cloud typically hosts large outsourced data of users in its storage. The verification cost should be efficient enough for practical use, i.e., it only depends on the corresponding search operation, regardless of the file collection size. In this paper, we are among the first to investigate the efficient search result verification problem and propose an encrypted data search scheme that enables users to conduct secure conjunctive keyword search, update the outsourced file collection and verify the authenticity of the search result efficiently. The proposed verification mechanism is efficient and flexible, which can be either delegated to a public trusted authority (TA) or be executed privately by data users. We formally prove the universally composable (UC) security of our scheme. Experimental result shows its practical efficiency even with a large dataset.

With the growing awareness of data privacy, more and more cloud users choose to encrypt their sensitive data before outsourcing them to the cloud. Search over encrypted data is therefore a critical function facilitating efficient cloud data access given the high data volume that each user has to handle nowadays. Inverted index is one of the most efficient searchable index structures and has been widely adopted in plaintext search. However, securing an inverted index and its associated search schemes is not a trivial task. A major challenge exposed from the existing efforts is the difficulty to protect user's query privacy. The challenge roots on two facts: 1) the existing solutions use a deterministic trapdoor generation function for queries; and 2) once a keyword is searched, the encrypted inverted list for this keyword is revealed to the cloud server. We denote this second property in the existing solutions as one-time-only search limitation. Additionally, conjunctive multi-keyword search, which is the most common form of query nowadays, is not supported in those works. In this paper, we propose a public-key searchable encryption scheme based on the inverted index. Our scheme preserves the high search efficiency inherited from the inverted index while lifting the one-time-only search limitation of the previous solutions. Our scheme features a probabilistic trapdoor generation algorithm and protects the search pattern. In addition, our scheme supports conjunctive multi-keyword search. Compared with the existing public key based schemes that heavily rely on expensive pairing operations, our scheme is more efficient by using only multiplications and exponentiations. To meet stronger security requirements, we strengthen our scheme with an efficient oblivious transfer protocol that hides the access pattern from the cloud. The simulation results demonstrate that our scheme is suitable for practical usage with moderate overhead.

Advanced botnets adopt a peer-to-peer (P2P) infrastructure for more resilient command and control (C&C). Traditional detection techniques become less effective in identifying bots that communicate via a P2P structure. In this paper, we present PeerClean, a novel system that detects P2P botnets in real time using only high-level features extracted from C&C network flow traffic. PeerClean reliably distinguishes P2P bot-infected hosts from legitimate P2P hosts by jointly considering flow-level traffic statistics and network connection patterns. Instead of working on individual connections or hosts, PeerClean clusters hosts with similar flow traffic statistics into groups. It then extracts the collective and dynamic connection patterns of each group by leveraging a novel dynamic group behavior analysis. Comparing with the individual host-level connection patterns, the collective group patterns are more robust and differentiable. Multi-class classification models are then used to identify different types of bots based on the established patterns. To increase the detection probability, we further propose to train the model with average group behavior, but to explore the extreme group behavior for the detection. We evaluate PeerClean on real-world flow records from a campus network. Our evaluation shows that PeerClean is able to achieve high detection rates with few false positives.

Recent advances in MIMO degree-of-freedom (DoF)models allow us to study MIMO in a multi-hop network environment. On the other hand, successive interference cancellation (SIC) is a powerful physical layer technique used in multi-user detection. Based on the strengths and weaknesses of MIMO DoF and SIC, we propose a marriage between these two techniques so that DoF-based interference cancellation (IC) and SIC can help each other as follows: (i) SIC is exploited to decode multiple received signals to conserve DoF resources in IC, and (ii) DoFIC resolves the potential SINR barrier that SIC may encounter. In this paper, we develop the necessary mathematical models to realize the two ideas in a multi-hop wireless network. Together with scheduling and routing constraints, we develop a cross-layer optimization framework with joint DoF IC and SIC. By applying the framework on a throughput maximization problem, we find that SIC and DoF IC can indeed offer significant performance improvement by addressing each other's limitation.

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud for sharing, which brings forth new challenges for outsourced data security and privacy. Attribute-based encryption (ABE) is a promising cryptographic primitive, which has been widely applied to design fine-grained access control system recently. However, ABE is criticized for its high scheme overhead as the computational cost grows with the complexity of the access formula. This disadvantage becomes more serious for mobile devices with constrained computing resources. Aiming at tackling the challenge above, we present a generic and efficient solution to implement attribute-based access control system by introducing secure outsourcing techniques into ABE. More precisely, two cloud service providers (CSPs), namely key generation-cloud service provider (KG-CSP) and decryption-cloud service provider (D-CSP) are introduced to perform the outsourced key-issuing and decryption on behalf of attribute authority and users respectively. In order to outsource heavy computation to both CSPs without private information leakage, we formalize an underlying primitive called outsourced ABE (OABE) and propose several constructions with outsourced decryption and key-issuing. Finally, extensive experiment demonstrates that with the help of KG-CSP and D-CSP, efficient key-issuing and decryption are achieved in our constructions.

Wireless energy transfer (WET) is a new technology that can be used to charge the batteries of sensor nodes without wires. Although wireless, WET does require a charging station to be brought to within reasonable range of a sensor node so that a good energy transfer efficiency can be achieved. On the other hand, it has been well recognized that data collection with a mobile base station has significant advantages over a static one. Given that a mobile platform is required for WET, a natural approach is to employ the same mobile platform to carry the base station for data collection. In this paper, we study the interesting problem of co-locating a wireless charger (for WET) and a mobile base station on the same mobile platform-the wireless charging vehicle (WCV). The WCV travels along a pre-planned path inside the sensor network. Our goal is to minimize energy consumption of the entire system while ensuring that 1) each sensor node is charged in time so that it will never run out of energy, and 2) all data collected from the sensor nodes are relayed to the mobile base station. We develop a mathematical model for this problem (OPT-t), which is time-dependent. Instead of solving OPT-t directly, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. Our results offer a solution on how to use a single mobile platform to address both WET and data collection in sensor networks.

The dominate spectrum sharing paradigm of today is interference avoidance, where a secondary network can use the spectrum only when such a use is not interfering with the primary network. However, with the advances of physical-layer technologies, the mindset of this paradigm is being challenged. This paper explores a new paradigm called “transparent coexistence” for spectrum sharing between primary and secondary nodes in a multihop network environment. Under this paradigm, the secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency is accomplished through a systematic interference cancelation (IC) by the secondary nodes without any impact on the primary network. Although such a paradigm has been studied in the information theory (IT) and communications (COMM) communities, it is not well understood in the wireless networking community, particularly for multihop networks. This paper offers an in-depth study of this paradigm in a multihop network environment and addresses issues such as scheduling (both in frequency channels and time slots) and IC (to/from primary network and within the secondary network). Through a rigorous modeling and formulation, problem formulation, solution development, and simulation results, we show that transparent coexistence paradigm offers significant improvement in terms of spectrum access and throughput performance as compared to the current prevailing interference avoidance paradigm.

Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

The notion of verifiable database (VDB) enables a resource-constrained client to securely outsource a very large database to an untrusted server so that it could later retrieve a database record and update it by assigning a new value. Also, any attempt by the server to tamper with the data will be detected by the client. Very recently, Catalano and Fiore [17] proposed an elegant framework to build efficient VDB that supports public verifiability from a new primitive named vector commitment. In this paper, we point out Catalano-Fiore's VDB framework from vector commitment is vulnerable to the so-called forward automatic update (FAU) attack. Besides, we propose a new VDB framework from vector commitment based on the idea of commitment binding. The construction is not only public verifiable but also secure under the FAU attack. Furthermore, we prove that our construction can achieve the desired security properties.

With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. In this paper, we investigate secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. For the first time, we utilize the sparse matrix to propose a new secure outsourcing algorithm of large-scale linear equations in the fully malicious model. Compared with the state-of-the-art algorithm, the proposed algorithm only requires (optimal) one round communication (while the algorithm requires $L$ rounds of interactions between the client and cloud server, where $L$ denotes the number of iteration in iterative methods). Furthermore, the client in our algorithm can detect the misbehavior of cloud server with the (optimal) probability 1. Therefore, our proposed algorithm is superior in both efficiency and checkability. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of our algorithm.

Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to a wireless sensor network (WSN). However, charging sensor nodes one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling show that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology and investigate whether it is a scalable technology to address energy issues in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. Based on charging range of the WCV, we propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing, and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy. Through numerical results, we demonstrate that our solution can indeed address the charging scalability problem in a WSN.

Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this paper makes the first attempt to formally address the problem of authorized data deduplication. Different from traditional deduplication systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new deduplication constructions supporting authorized duplicate check in a hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement a prototype of our proposed authorized duplicate check scheme and conduct testbed experiments using our prototype. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.

Linear programming (LP) has been well studied in the scientific community for various engineering applications such as network flow problems, packet routing, portfolio optimization, and financial data management, etc. In this paper, we first utilize the sparse matrix to investigate secure outsourcing for large-scale LP systems, which is considered as a prohibitively expensive computation for the clients with resource-constraint devices. Besides, we propose a secure and practical scheme which is suitable for any LP problem (feasible, infeasible or unbounded) even in the fully malicious model. Compared with the state-of-the-art algorithm [30], our proposed algorithm only requires O(n2) computational overhead instead of O(nρ) for 2 <; ρ ≤ 3. Furthermore, the client C can detect the misbehavior of cloud server S with the (optimal) probability 1 under the computational complexity of O(n).

Cloud computing has become the real trend of enterprise IT service model that offers cost-effective and scalable processing. Meanwhile, Software-Defined Networking (SDN) is gaining popularity in enterprise networks for flexibility in network management service and reduced operational cost. There seems a trend for the two technologies to go hand-in-hand in providing an enterprise's IT services. However, the new challenges brought by the marriage of cloud computing and SDN, particularly the implications on enterprise network security, have not been well understood. This paper sets to address this important problem. We start by examining the security impact, in particular, the impact on DDoS attack defense mechanisms, in an enterprise network where both technologies are adopted. We find that SDN technology can actually help enterprises to defend against DDoS attacks if the defense architecture is designed properly. To that end, we propose a DDoS attack mitigation architecture that integrates a highly programmable network monitoring to enable attack detection and a flexible control structure to allow fast and specific attack reaction. The simulation results show that our architecture can effectively and efficiently address the security challenges brought by the new network paradigm.

This paper explores a new paradigm for the coexistence among heterogeneous multi-hop networks in unplanned deployment settings, called cooperative interference mitigation (CIM). CIM exploits recent advancements in physical layer technologies such as technology-independent multiple output (TIMO), making it possible for disparate networks to cooperatively mitigate the interference to each other to enhance everyone's performance, even if they possess different wireless technologies. This paper offers a thorough study of the CIM paradigm for unplanned multi-hop networks. We first show the feasibility of CIM among heterogeneous multi-hop networks by exploiting only channel ratio information, and then establish a tractable model to accurately characterize the CIM behaviors of both networks. We also develop a bi-criteria optimization formulation to maximize both networks' throughput, and propose a new methodology to compute the Pareto-optimal throughput curve as performance bound. Simulation results show that CIM provides significant performance gains to both networks compared with the traditional interference-avoidance paradigm.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e. multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. Finally, performance evaluation shows the efficiency of our scheme.

Enabling keyword search directly over encrypted data is a desirable technique for effective utilization of encrypted data outsourced to the cloud. Existing solutions provide multi-keyword exact search that does not tolerate keyword spelling error, or single keyword fuzzy search that tolerates typos to certain extent. The current fuzzy search schemes rely on building an expanded index that covers possible keyword misspelling, which lead to significantly larger index file size and higher search complexity. In this paper, we propose a novel multi-keyword fuzzy search scheme by exploiting the locality-sensitive hashing technique. Our proposed scheme achieves fuzzy matching through algorithmic design rather than expanding the index file. It also eliminates the need of a predefined dictionary and effectively supports multiple keyword fuzzy search without increasing the index or search complexity. Extensive analysis and experiments on real-world data show that our proposed scheme is secure, efficient and accurate. To the best of our knowledge, this is the first work that achieves multi-keyword fuzzy search over encrypted cloud data.

Reactive jamming is considered the most powerful jamming attack as the attack efficiency is maximized while the risk of being detected is minimized. Currently, there are no effective anti-jamming solutions to secure OFDM wireless communications under reactive jamming attack. On the other hand, MIMO has emerged as a technology of great research interest in recent years mostly due to its capacity gain. In this paper, we explore the use of MIMO technology for jamming resilient OFDM communication, especially its capability to communicate against the powerful reactive jammer. We first investigate the jamming strategies and their impacts on the OFDM-MIMO receivers. We then present a MIMO-based anti-jamming scheme that exploits interference cancellation and transmit precoding capabilities of MIMO technology to turn a jammed non-connectivity scenario into an operational network. Our testbed evaluation shows the destructive power of reactive jamming attack, and also validates the efficacy and efficiency of our defense mechanisms.

Transparent coexistence, also known as underlay, offers much more efficient spectrum sharing than traditional interweave coexistence paradigm. In a previous work, the transparent coexistence for a multi-hop secondary networks is studied. In this paper, we design a distributed solution to achieve this paradigm. In our design, we show how to increase the number of data streams iteratively while meeting constraints in the MIMO interference cancelation (IC) model and achieving transparent coexistence. All steps in our distributed algorithm can be accomplished based on local information exchange among the neighboring nodes. Our simulation results show that the performance of our distributed algorithm is highly competitive when compared to an upper bound solution for the centralized problem.

Recent advances in information theory (IT) have shown great promises of interference alignment (IA) for cellular networks. However, due to a number of assumptions, these IT results cannot be directly applied to address practical problems. The goal of this paper is to fill in this gap by studying IA for cellular networks with more practical settings. We propose an IA scheme that includes constraints at each user and each base station (BS) for the uplink communication of a cellular network. We prove the feasibility of the IA scheme by constructing the encoding and decoding vectors for each data stream so that it can be transported free of interference. Based on this IA scheme, we study an uplink user throughput maximization problem and show the throughput improvement of the IA scheme over two other schemes.

With the rapid development of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attention in the scientific community. Exponentiations modulo a large prime have been considered the most expensive operations in discrete-logarithm-based cryptographic protocols, and they may be burdensome for the resource-limited devices such as RFID tags or smartcards. Therefore, it is important to present an efficient method to securely outsource such operations to (untrusted) cloud servers. In this paper, we propose a new secure outsourcing algorithm for (variable-exponent, variable-base) exponentiation modulo a prime in the two untrusted program model. Compared with the state-of-the-art algorithm, the proposed algorithm is superior in both efficiency and checkability. Based on this algorithm, we show how to achieve outsource-secure Cramer-Shoup encryptions and Schnorr signatures. We then propose the first efficient outsource-secure algorithm for simultaneous modular exponentiations. Finally, we provide the experimental evaluation that demonstrates the efficiency and effectiveness of the proposed outsourcing algorithms and schemes.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of "coordinate matching," i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.

With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework, i.e., SpecMonitor, for traffic collection using a limited number of sniffers in Wi-Fi-like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a nonparametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals based on the predicted channel access patterns. The simulations and experiments show that SpecMonitor outperforms the existing schemes significantly.

With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

In this paper, we propose a privacy-preserving proximity-based security strategy for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. More specifically, radio clients build their location tags according to the unique physical features of their ambient radio signals, which cannot be forged by attackers outside the proximity range. The proximity-based authentication and session key generation is based on the public location tag, which incorporates the received signal strength indicator (RSSI), sequence number and MAC address of the ambient radio packets. Meanwhile, as the basis for the session key generation, the secret location tag consisting of the arrival time interval of the ambient packets, is never broadcast, making it robust against eavesdroppers and spoofers. The proximity test utilizes the nonparametric Bayesian method called infinite Gaussian mixture model, and provides range control by selecting different features of various ambient radio sources. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.

Lacking trusted central authority, distributed systems have received serious security threats from Sybil attack, where an adversary forges identities of more than one node and attempts to control the system. By utilizing the real-world trust relationships between users, social network-based defense schemes have been proposed to mitigate the impact of Sybil attacks. These solutions are mostly built on the assumption that the social network graph can be partitioned into two loosely linked regions - a tightly connected non-Sybil region and a Sybil region. Although such an assumption may hold in certain settings, studies have shown that the real-world social connections tend to divide users into multiple inter-connected small worlds instead of a single uniformly connected large region. Given this fact, the applicability of existing schemes would be greatly undermined for inability to distinguish Sybil users from valid ones in the small non-Sybil regions. This paper addresses this problem and presents SybilShield, the first protocol that defends against Sybil attack utilizing multi-community social network structure in real world. Our scheme leverages the sociological property that the number of cutting edges between a non-Sybil community and a Sybil community, which represent human-established trust relationships, is much smaller than that among non-Sybil communities. With the help of agent nodes, SybilShield greatly reduces false positive rate of non-Sybils among multiple communities, while effectively identifying Sybil nodes. Analytical results prove the superiority of SybilShield. Our experiments on a real-world social network graph with 100,000 nodes also validate the effectiveness of SybilShield.

Wireless energy transfer is a promising technology to fundamentally address energy and lifetime problems in a wireless sensor network (WSN). On the other hand, it has been well recognized that a mobile base station has significant advantages over a static one. In this paper, we study the interesting problem of co-locating the mobile base station on the wireless charging vehicle (WCV). The goal is to minimize energy consumption of the entire system while ensuring none of the sensor nodes runs out of energy. We develop a mathematical model for this complex problem. Instead of studying the general problem formulation (OPT-t), which is time-dependent, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. The novelty of this research mainly resides in the development of several solution techniques to tackle a complex problem that is seemingly intractable at first glance. In addition to addressing a challenging and interesting problem in a WSN, we expect the techniques developed in this research can be applied to address other related networking problems involving time-dependent movement, flow routing, and energy consumption.

Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored, and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework for traffic collection using a limited number of sniffers in WiFi like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a non-parametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals respectively, based on the predicted channel access patterns. The simulations and experiments show that our proposed framework outperforms the existing schemes significantly.

Degree-of-Freedom (DoF)-based model is a simple yet powerful tool to analyze MIMO's spatial multiplexing (SM) and interference cancellation (IC) capabilities in a multi-hop network. Recently, a new DoF model was proposed and was shown to achieve the same rate region as the matrix-based model (under SM and IC). The essence of this new DoF model is a novel node ordering concept, which eliminates potential duplication of DoF allocation for IC. In this paper, we investigate DoF scheduling for a multi-hop MIMO network based on this new DoF model. Specifically, we study how to perform DoF allocation among the nodes for SM and IC so as to maximize the minimum rate among a set of sessions. We formulate this problem as a mixed integer linear programming (MILP) and develop an efficient DoF scheduling algorithm to solve it. We show that our algorithm is amenable to local implementation and has polynomial time complexity. More importantly, it guarantees the feasibility of final solution (upon algorithm termination), despite that node ordering establishment and adjustment are performed locally. Simulation results show that our algorithm can offer a result that is close to an upper bound found by CPLEX solver, thus showing that the result found by our algorithm is highly competitive.

Interference alignment (IA) is a major advance in information theory. Despite its rapid advance in the information theory community, most results on IA remain point-to-point or single-hop and there is a lack of advance of IA in the context of multi-hop wireless networks. The goal of this paper is to make a concrete step toward advancing IA technique in multi-hop MIMO networks. We present an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine a subset of interfering streams for IA. Based on this IA model, we develop an IA optimization framework for a multihop MIMO network. For performance evaluation, we compare the performance of a network throughput optimization problem under our proposed IA framework and the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

There has been a growing interest to employ the so-called degree-of-freedom (DoF) based models to study multihop MIMO networks. Existing DoF-based models differ in their interference cancelation (IC) behavior and suffer from either loss of solution space or possible infeasible solutions. Recently, a DoF model based on a novel node-ordering concept was proposed to overcome the limitations of the exiting DoF models. In this paper, we apply this new DoF model to study a throughput maximization problem in a multi-hop network. The problem formulation jointly considers half duplex, node ordering, DoF consumption constraints and flow routing and is in the form of a mixed integer linear program (MILP). Our main contribution is the development of an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The key idea in the algorithm is to explore (i) the impact of node ordering on DoF consumption for IC at a node, and (ii) route diversity in the network while ensuring DoF constraints are satisfied at each node throughout the iterations. Simulation results show that our solutions by the proposed algorithm are competitive and feasible.

The dominant spectrum sharing paradigm of today is the interweave paradigm. This paper advocates a new and alternative paradigm called United network of Primary and Secondary networks (UPS). UPS allows a complete cooperation between primary and secondary networks at the node level to relay each other's traffic, in addition to existing dynamic spectrum access (DSA) in time, space, and frequency domains. Such cooperation allows the primary and secondary networks to access a much richer network resources from the combined network. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the minimum throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. Although this problem is in the form of mixed integer linear program (MILP), we can use CPLEX to solve it efficiently. Simulation results show that the UPS paradigm offers much better throughput performance than the interweave DSA paradigm.

Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.

This paper explores the so-called “transparent coexistence” paradigm for spectrum sharing between primary and secondary nodes in a multi-hop network environment. Although such paradigm has been studied in the information theory and communications communities, it is not well understood in the wireless networking community, particularly for multihop networks. Under this paradigm, a secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency can be accomplished through a systematic interference cancellation (IC) by the secondary nodes without any impact on the primary network. This paper offers an in-depth study of this paradigm in a multi-hop network environment and addresses issues such as channel selection, IC to/from primary network, and IC within the secondary network. Through a rigorous modeling and formulation, we develop an optimization problem under this paradigm with the objective of maximizing secondary user's throughput. Through simulation results, we show that such paradigm offers significant improvement to a multi-hop network in terms of spectrum efficiency and throughput performance as compared to the prevailing interference-avoidance paradigm.

Using cloud storage, users can remotely store their data and enjoy the on-demand high-quality applications and services from a shared pool of configurable computing resources, without the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in cloud computing a formidable task, especially for users with constrained computing resources. Moreover, users should be able to just use the cloud storage as if it is local, without worrying about the need to verify its integrity. Thus, enabling public auditability for cloud storage is of critical importance so that users can resort to a third-party auditor (TPA) to check the integrity of outsourced data and be worry free. To securely introduce an effective TPA, the auditing process should bring in no new vulnerabilities toward user data privacy, and introduce no additional online burden to user. In this paper, we propose a secure cloud storage system supporting privacy-preserving public auditing. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently. Extensive security and performance analysis show the proposed schemes are provably secure and highly efficient. Our preliminary experiment conducted on Amazon EC2 instance further demonstrates the fast performance of the design.

In this paper, we propose a privacy-preserving proximity-based security system for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority, or public key infrastructure. In this system, the proximity-based authentication and session key establishment are implemented based on spatial temporal location tags. Incorporating the unique physical features of the signals sent from multiple ambient radio sources, the location tags cannot be easily forged by attackers. More specifically, each radio client builds a public location tag according to the received signal strength indicators, sequence numbers, and media access control (MAC) addresses of the ambient packets. Each client also keeps a secret location tag that consists of the packet arrival time information to generate the session keys. As clients never disclose their secret location tags, this system is robust against eavesdroppers and spoofers outside the proximity range. The system improves the authentication accuracy by introducing a nonparametric Bayesian method called infinite Gaussian mixture model in the proximity test and provides flexible proximity range control by taking into account multiple physical-layer features of various ambient radio sources. Moreover, the session key establishment strategy significantly increases the key generation rate by exploiting the packet arrival time of the ambient signals. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.

The body area network (BAN) is a key enabling technology in e-healthcare. An important security issue is to establish initial trust relationships among the BAN devices before they are actually deployed and generate necessary shared secret keys to protect the subsequent wireless communications. Due to the ad hoc nature of the BAN and the extreme resource constraints of sensor devices, providing secure as well as efficient and user-friendly trust initialization is a challenging task. Traditional solutions for wireless sensor networks mostly depend on key predistribution, which is unsuitable for a BAN in many ways. In this article, we propose group device pairing (GDP), a user-aided multi-party authenticated key agreement protocol. Through GDP, a group of sensor devices that have no pre-shared secrets establish initial trust by generating various shared secret keys out of an unauthenticated channel. Devices authenticate themselves to each other with the aid of a human user who performs visual verifications. The GDP supports fast batch deployment, addition and revocation of sensor devices, does not rely on any additional hardware device, and is mostly based on symmetric key cryptography. We formally prove the security of the proposed protocols, and we implement GDP on a sensor network testbed and report performance evaluation results.

Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. To assure the patients' control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access, and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semitrusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute-based encryption (ABE) techniques to encrypt each patient's PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting multiauthority ABE. Our scheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability, and efficiency of our proposed scheme.

The cost of energy consumption is an important concern for network operators. In this paper, we study an energy-related problem that focuses on network-wide energy consumption. In the first part of this work, we study how to maximize throughput under a network-wide energy constraint. We formulate this problem as a mixed-integer nonlinear program (MINLP). This formulation differs from prior efforts as it considers a non-zero device power, which complicates the problem. We propose a novel piece-wise linear approximation to transform the nonlinear constraints into linear constraints. We prove that the solution developed under this approach is near-optimal with a guaranteed performance bound. In the second part, we generalize the problem in the first part via a multicriteria optimization framework, which simultaneously optimizes throughput and total network energy. We show how weakly Pareto-optimal solutions can characterize an optimal throughput-energy curve. We offer some interesting properties of the optimal throughput-energy curves, which are useful to both network operators and end-users. Our results fill in some important gaps in the current understanding on optimizing total network energy.

Making new connections according to personal preferences is a crucial service in mobile social networking, where an initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, a set of privacy-preserving profile matching schemes for proximity-based mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Two increasing levels of user privacy are defined, with decreasing amounts of revealed profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize each of the user privacy levels, which can also be personalized by the users. We provide formal security proofs and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.

With the increasing adoption of cloud computing for data storage, assuring data service reliability, in terms of data correctness and availability, has been outstanding. While redundancy can be added into the data for reliability, the problem becomes challenging in the “pay-as-you-use” cloud paradigm where we always want to efficiently resolve it for both corruption detection and data repair. Prior distributed storage systems based on erasure codes or network coding techniques have either high decoding computational cost for data users, or too much burden of data repair and being online for data owners. In this paper, we design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, this paper proposes an exact repair solution so that no metadata needs to be generated on the fly for repaired data. The performance analysis and experimental results show that our designed service has comparable storage and communication cost, but much less computational cost during data retrieval than erasure codes-based storage solutions. It introduces less storage cost, much faster data retrieval, and comparable communication cost comparing to network coding-based distributed storage systems.

Conserving network-wide energy consumption is becoming an increasingly important concern for network operators. In this work, we study network-wide energy conservation problem which we hope will offer insights to both network operators and users. In the first part of this work, we study how to maximize throughput under a network-wide energy constraint. We formulate this problem as a mixed-integer nonlinear program (MINLP). We propose a novel piece-wise linear approximation to transform the nonlinear constraints into linear constraints. We prove that the solution developed under this approach is near-optimal with guaranteed performance bound. In the second part, we generalize the problem in the first part by exploring throughput and network-wide energy optimization via a multi-criteria optimization framework. We show that the weakly Pareto-optimal points in the solution can characterize an optimal throughput-energy curve. We offer some interesting properties of the optimal throughput-energy curve which are useful to both network operators and end users.

There is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we try to answer the following fundamental questions. What are the limitations of SIC? How to overcome such limitations? How to optimize the interaction between SIC and interference avoidance? How to incorporate multiple layers (physical, link, and network) in an optimization framework? We find that SIC alone is not adequate to handle interference in a multi-hop wireless network, and advocate the use of joint SIC and interference avoidance. To optimize a joint scheme, we propose a cross-layer optimization framework that incorporates variables at physical, link, and network layers. This is the first work that combines successive interference cancellation and interference avoidance in multi-hop wireless network. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC and interference avoidance can complement each other in an optimal manner.

Capacity scaling laws offer fundamental understanding on the trend of user throughput behavior when the network size increases. Since the seminal work of Gupta and Kumar, there have been active research efforts in developing capacity scaling laws for ad hoc networks under various advanced physical layer technologies. These efforts led to many custom-designed solutions, most of which were intellectually challenging and lacked universal properties that can be extended to address scaling laws of ad hoc networks with other physical layer technologies. In this paper, we present a set of simple yet powerful tool that can be applied to quickly determine the capacity scaling laws for various physical layer technologies under the protocol model. We prove the correctness of our proposed criteria and demonstrate their usage through a number of case studies, such as ad hoc networks with directional antenna, MIMO, multi-channel multi-radio, cognitive radio, and multiple packet reception. These simple criteria will serve as powerful tools to networking researchers to obtain throughput scaling laws of ad hoc networks under different physical layer technologies, particularly those to be developed in the future.

Cooperative spectrum sensing is key to the success of cognitive radio networks. Recently, fully distributed cooperative spectrum sensing has been proposed for its high performance benefits particularly in cognitive radio ad hoc networks. However, the cooperative and fully distributed natures of such protocol make it highly vulnerable to malicious attacks, and make the defense very difficult. In this paper, we analyze the vulnerabilities of distributed sensing architecture based on a representative distributed consensus-based spectrum sensing algorithm. We find that such distributed algorithm is particularly vulnerable to a novel form of attack called covert adaptive data injection attack. The vulnerabilities are even magnified under multiple colluding attackers. We further propose effective protection mechanisms, which include a robust distributed outlier detection scheme with adaptive local threshold to thwart the covert adaptive data injection attack, and a hash-based computation verification approach to cope with collusion attacks. Through simulation and analysis, we demonstrate the destructive power of the attacks, and validate the efficacy and efficiency of our proposed protection mechanisms.

Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to sensor nodes in a wireless sensor network (WSN). However, charging sensor node one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling shows that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology to address energy issue in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. We propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. The WCV visits these cells and charge sensor nodes from the center of a cell. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy.

This paper presents a theoretical study of the throughput of mobile content distribution (MCD) in vehicular ad hoc networks (VANETs). Since VANET is well-known for its fast-changing topology and adverse wireless channel environments, various protocols have been proposed in the literature to enhance the performance of MCD in a vehicular environment, using packet-level network coding (PLNC) and symbol-level network coding (SLNC). However, there still lacks a fundamental understanding of the limits of MCD protocols using network coding in VANETs. In this paper, we develop a theoretical model to compute the achievable throughput of cooperative MCD in VANETs using SLNC. By considering a one-dimensional road topology with an access point (AP) as the content source, the expected achievable throughput for a vehicle at a certain distance from the AP is derived, for both using PLNC and SLNC. Our proposed model is unique since it captures the effects of multiple practical factors, including vehicle distribution and mobility pattern, channel fading and packet collisions. Through numerical results, we provide insights on optimized design choices for network coding-based cooperative MCD systems in VANETs.

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys “as-strong-as-possible” security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, such a service is also relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with very lightweight communication and computation cost. The auditing result not only ensures strong cloud storage correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.

The fundamental challenges of providing live multimedia streaming (LMS) services in vehicular ad hoc networks (VANETs) come from achieving stable and high streaming rate (smooth playback) for all the interested vehicles while using minimal bandwidth resources, especially under the highly dynamic topology of VANETs and the lossy nature of vehicular wireless communications. Packet level network coding (PLNC) technique has been widely accepted as an effective approach to improve the network performance during the last decade. More recent symbol-level network coding (SLNC) could further improve the efficiency of bandwidth utilization by exploiting both wireless symbol-level diversity and the benefits of network coding. In this paper, we introduce CodePlay, a new LMS scheme in VANETs that fully takes advantage of SLNC through a coordinated local push mechanism. Streaming contents are actively disseminated from dedicated sources to interested vehicles via local coordination of distributively selected relays, each of which will ensure smooth playback for vehicles nearby. Extensive simulations show that simply replacing the SLNC with PLNC technique in previous LMS schemes can not provide satisfiable user experience, and special scheme design based on the unique characteristics of SLNC proposed in CodePlay is necessary for future LMS applications in VANET.

With recent advances in communication and data storage technology, an explosive amount of information is being collected and stored in the Internet. Even though such vast amount of information presents great opportunities for knowledge discovery, organizations might not want to share their data due to legal or competitive reasons. This posts the challenge of mining knowledge while preserving privacy. Current efficient privacy-preserving data mining algorithms are based on an assumption that it is acceptable to release all the intermediate results during the data mining operations. However, it has been shown that such intermediate results can still leak private information. In this work, we use differential privacy to quantitatively limit such information leak. Differential privacy is a newly emerged privacy definition that is capable of providing strong measurable privacy guarantees. We propose Secure group Differential private Query (SDQ), a new algorithm that combines techniques from differential privacy and secure multiparty computation. Using decision tree induction as a case study, we show that SDQ can achieve stronger privacy than current efficient secure multiparty computation approach, and better accuracy than current differential privacy approach while maintaining efficiency.

In the emerging cloud computing paradigm, data owners become increasingly motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users' privacy, sensitive data have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, for the first time, we define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing (PPGQ), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Our work utilizes the principle of "filtering-and-verification". We prebuild a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure. To meet the challenge of supporting graph query without privacy breaches, we propose a secure inner product computation technique, and then improve it to achieve various privacy requirements under the known-background threat model.

In cloud computing, clients usually outsource their data to the cloud storage servers to reduce the management costs. While those data may contain sensitive personal information, the cloud servers cannot be fully trusted in protecting them. Encryption is a promising way to protect the confidentiality of the outsourced data, but it also introduces much difficulty to performing effective searches over encrypted information. Most existing works do not support efficient searches with complex query conditions, and care needs to be taken when using them because of the potential privacy leakages about the data owners to the data users or the cloud server. In this paper, using on line Personal Health Record (PHR) as a case study, we first show the necessity of search capability authorization that reduces the privacy exposure resulting from the search results, and establish a scalable framework for Authorized Private Keyword Search (APKS) over encrypted cloud data. We then propose two novel solutions for APKS based on a recent cryptographic primitive, Hierarchical Predicate Encryption (HPE). Our solutions enable efficient multi-dimensional keyword searches with range query, allow delegation and revocation of search capabilities. Moreover, we enhance the query privacy which hides users' query keywords against the server. We implement our scheme on a modern workstation, and experimental results demonstrate its suitability for practical usage.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.

Making new connections according to personal preferences is a crucial service in mobile social networking, where the initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose FindU, the first privacy-preserving personal profile matching schemes for mobile social networks. In FindU, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Several increasing levels of user privacy are defined, with decreasing amounts of exchanged profile information. Leveraging secure multi-party computation (SMC) techniques, we propose novel protocols that realize two of the user privacy levels, which can also be personalized by the users. We provide thorough security analysis and performance evaluation on our schemes, and show their advantages in both security and efficiency over state-of-the-art schemes.

Driven by both safety concerns and commercial interests, one of the key services offered by vehicular networks is popular content distribution (PCD). The fundamental challenges to achieve high speed content downloading come from the highly dynamic topology of vehicular ad hoc network (VANET) and the lossy nature of the vehicular wireless communications. In this paper, we introduce CodeOn, a novel push-based PCD scheme where contents are actively broadcasted to vehicles from road side access points and further distributed among vehicles using a cooperative VANET. In CodeOn, we employ a recent technique, symbol level network coding (SLNC) to combat the lossy wireless transmissions. Through exploiting symbol level diversity, SLNC is robust to transmission errors and encourages more aggressive concurrent transmissions. In order to fully enjoy the benefits of SLNC, we propose a suite of techniques to maximize the downloading rate, including a prioritized and localized relay selection mechanism where the selection criteria is based on the usefulness of vehicles' possessed contents, and a lightweight medium access protocol that naturally exploits the abundant concurrent transmission opportunities. We also propose additional mechanisms to reduce the protocol overhead without sacrificing the performance. Extensive simulation results show that, under a wide range of scenarios, CodeOn significantly outperforms a state-of-the-art PCD scheme based on network coding.

Recently, distributed data storage has gained increasing popularity for efficient and robust data management in wireless sensor networks (WSNs). The distributed architecture makes it challenging to build a highly secure and dependable yet lightweight data storage system. On the one hand, sensor data are subject to not only Byzantine failures, but also dynamic pollution attacks, as along the time the adversary may modify/pollute the stored data by compromising individual sensors. On the other hand, the resource-constrained nature of WSNs precludes the applicability of heavyweight security designs. To address the challenge, in this article we propose a novel dependable and secure data storage scheme with dynamic integrity assurance. Based on the principle of secret sharing and erasure coding, we first propose a hybrid share generation and distribution scheme to achieve reliable and fault-tolerant initial data storage by providing redundancy for original data components. To further dynamically ensure the integrity of the distributed data shares, we then propose an efficient data integrity verification scheme exploiting the techniques of algebraic signature and spot-checking. The proposed scheme enables individual sensors to verify in one protocol execution the correctness of all the pertaining data shares simultaneously in the absence of the original data. Extensive security analysis shows that the proposed scheme has strong resistance against various data pollution attacks. The efficiency of the scheme is demonstrated by experiments on sensor platforms Tmote Sky and iMote2.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of the client through the auditing of whether his data stored in the cloud are indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion, and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public auditability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for the seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the existing proof of storage models by manipulating the classic Merkle Hash Tree construction for block tag authentication. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis show that the proposed schemes are highly efficient and provably secure.

Distributed sensor data storage and retrieval have gained increasing popularity in recent years for supporting various applications. While distributed architecture enjoys a more robust and fault-tolerant wireless sensor network (WSN), such architecture also poses a number of security challenges especially when applied in mission-critical applications such as battlefield and e-healthcare. First, as sensor data are stored and maintained by individual sensors and unattended sensors are easily subject to strong attacks such as physical compromise, it is significantly harder to ensure data security. Second, in many mission-critical applications, fine-grained data access control is a must as illegal access to the sensitive data may cause disastrous results and/or be prohibited by the law. Last but not least, sensor nodes usually are resource-constrained, which limits the direct adoption of expensive cryptographic primitives. To address the above challenges, we propose, in this paper, a distributed data access control scheme that is able to enforce fine-grained access control over sensor data and is resilient against strong attacks such as sensor compromise and user colluding. The proposed scheme exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for WSNs with respect to both performance and security requirements. The feasibility of the scheme is demonstrated by experiments on real sensor platforms. To our best knowledge, this paper is the first to realize distributed fine-grained data access control for WSNs.

Ciphertext-Policy Attribute Based Encryption (CP-ABE) is a promising cryptographic primitive for fine-grained access control of shared data. In CP-ABE, each user is associated with a set of attributes and data are encrypted with access structures on attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the ciphertext access structure. Beside this basic property, practical applications usually have other requirements. In this paper we focus on an important issue of attribute revocation which is cumbersome for CP-ABE schemes. In particular, we resolve this challenging issue by considering more practical scenarios in which semi-trustable on-line proxy servers are available. As compared to existing schemes, our proposed solution enables the authority to revoke user attributes with minimal effort. We achieve this by uniquely integrating the technique of proxy re-encryption with CP-ABE, and enable the authority to delegate most of laborious tasks to proxy servers. Formal analysis shows that our proposed scheme is provably secure against chosen ciphertext attacks. In addition, we show that our technique can also be applicable to the Key-Policy Attribute Based Encryption (KP-ABE) counterpart.

The technique of distributed storage coding has been widely used in wireless sensor networks for increasing the robustness of data storage and efficiency of data retrieval. Existing works mainly focus on scenarios in which each storage node stores a linear combination of a subset of K data packets generated by different source nodes. By solving the linear equations, a collector can recover all the K data packets with high probability. This paper explores the problem of flexible and efficient data dissemination and retrieval in wireless sensor networks. Our scheme exploits the broadcast nature of wireless transmission and improves the traditional random walk algorithm for efficient data dissemination. Furthermore, through the use of Fountain codes in data encoding, it enables a mobile collector to recover up-to-date data generated by any subset of source nodes. Specifically, by querying any t(1 + ε) storage nodes at the t-th time slot, the target data can be retrieved without having to decode all the source packets. Simulation results validate our analysis and show that the proposed schemes are flexible and efficient.

As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys ``as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Live multimedia streaming (LMS) services are important in vehicular ad hoc networks (VANETs) for their capability of providing comprehensive and user-friendly information. The fundamental challenges come from achieving stable and high streaming rate (smooth playback) for all the interested vehicles while using minimal bandwidth resources, especially under the highly dynamic topology of VANETs and the lossy nature of vehicular wireless communications. A recent technique, symbol-level network coding (SLNC), has been shown to be an effective approach to improve the efficiency of bandwidth utilization, by exploiting both wireless symbol-level diversity and the benefits of network coding. In this paper, we introduce CodePlay, a new LMS scheme in VANETs that fully takes advantage of SLNC through a coordinated local push mechanism. Streaming contents are actively disseminated from dedicated sources to interested vehicles via local coordination of distributively selected relays, each of which will ensure smooth playback for vehicles nearby. CodePlay is designed to simultaneously improve the performance of LMS service in terms of streaming rate, service delivery delay and bandwidth efficiency. We use extensive simulations to show that CodePlay is potentially suitable for future LMS applications in VANET.

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users' searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.

Body Area Networks (BAN) is a key enabling technology in E-healthcare such as remote health monitoring. An important security issue during bootstrap phase of the BAN is to securely associate a group of sensor nodes to a patient, and generate necessary secret keys to protect the subsequent wireless communications. Due to the the ad hoc nature of the BAN and the extreme resource constraints of sensor devices, providing secure, fast, efficient and user-friendly secure sensor association is a challenging task. In this paper, we propose a lightweight scheme for secure sensor association and key management in BAN. A group of sensor nodes, having no prior shared secrets before they meet, establish initial trust through group device pairing (GDP), which is an authenticated group key agreement protocol where the legitimacy of each member node can be visually verified by a human. Various kinds of secret keys can be generated on demand after deployment. The GDP supports batch deployment of sensor nodes to save setup time, does not rely on any additional hardware devices, and is mostly based on symmetric key cryptography, while allowing batch node addition and revocation. We implemented GDP on a sensor network testbed and evaluated its performance. Experimental results show that that GDP indeed achieves the expected design goals.

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.

Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. As promising as it is, this paradigm also brings forth many new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are not within the same trusted domain as data owners. To keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. The problem of simultaneously achieving fine-grainedness, scalability, and data confidentiality of access control actually still remains unresolved. This paper addresses this challenging open issue by, on one hand, defining and enforcing access policies based on data attributes, and, on the other hand, allowing the data owner to delegate most of the computation tasks involved in fine-grained data access control to untrusted cloud servers without disclosing the underlying data contents. We achieve this goal by exploiting and uniquely combining techniques of attribute-based encryption (ABE), proxy re-encryption, and lazy re-encryption. Our proposed scheme also has salient properties of user access privilege confidentiality and user secret key accountability. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security models.

Two major factors that limit the throughput in multi-hop wireless networks are the unreliability of wireless transmissions and co-channel interference. One promising technique that combats lossy wireless transmissions is opportunistic routing (OR). OR involves multiple forwarding candidates to relay packets by taking advantage of the broadcast nature and spacial diversity of the wireless medium. Furthermore, recent advances in multi-radio multi-channel transmission technology allows more concurrent transmissions in the network, and shows the potential of substantially improving the system capacity. However, the performance of OR in multi-radio multi-channel systems is still unknown, and the methodology of studying the performance of traditional routing (TR) can not be directly applied to OR. In this paper, we present our research on computing an end-to-end throughput bound of OR in multi-radio multi-channel systems. We formulate the capacity of OR as a linear programming (LP) problem which jointly solves the radio-channel assignment and transmission scheduling. Leveraging our analytical model, we gain the following insights into OR: 1) OR can achieve better performance than TR under different radio/channel configurations, however, in particular scenarios, TR is more preferable than OR; 2) OR can achieve comparable or even better performance than TR by using less radio resource; 3) for OR, the throughput gained from increasing the number of potential forwarding candidates becomes marginal.

Recently, multihop wireless mesh networks (WMNs) have attracted increasing attention and deployment as a low-cost approach to provide broadband Internet access at metropolitan scale. Security and privacy issues are of most concern in pushing the success of WMNs for their wide deployment and for supporting service-oriented applications. Despite the necessity, limited security research has been conducted toward privacy preservation in WMNs. This motivates us to develop PEACE, a novel Privacy-Enhanced yet Accountable seCurity framEwork, tailored for WMNs. On one hand, PEACE enforces strict user access control to cope with both free riders and malicious users. On the other hand, PEACE offers sophisticated user privacy protection against both adversaries and various other network entities. PEACE is presented as a suite of authentication and key agreement protocols built upon our proposed short group signature variation. Our analysis shows that PEACE is resilient to a number of security and privacy related attacks. Additional techniques were also discussed to further enhance scheme efficiency.

Two major factors that limit the throughput in multi-hop wireless networks are the co-channel interference and unreliability of wireless transmissions. Multi-radio multi-channel technology and opportunistic routing (OR) have shown their promise to significantly improve the network capacity by combating these two limits. It raises an interesting problem on the tradeoff between multiplexing and spatial diversity when integrating these two techniques for throughput optimization. It is unknown what the capacity of the network could be when nodes have multiple radios and OR capability. In this paper, we present our study on optimizing an end-to-end throughput of the multi-radio multi-channel network when OR is available. First, we formulate the end-to-end throughput bound as a linear programming (LP) problem which jointly solves the radio-channel assignment, transmission scheduling, and forwarding candidate selection. Second, we propose an LP approach and a heuristic algorithm to find a feasible scheduling of opportunistic forwarding priorities to achieve the capacity. Simulations show that the heuristic algorithm achieves desirable performance under various number of forwarding candidates. Leveraging our analytical model, we find that 1) OR can achieve better performance than traditional routing (TR) under different radio/channel configurations, however, in particular scenario (e.g. bottleneck links exist between the sender and relays), TR is preferable; 2) OR can achieve comparable or better performance than TR by using less radio resource.

Broadcast is an important primitive in wireless mesh networks (WMNs). Applications like network-wide software update require reliable reception of the content with low-latency and high scalability (i.e., utilizing little bandwidth resource). In reality, the link layer broadcast transmission in WMNs is unreliable, which makes these goals hard to be attained at the same time. In this paper, we consider one-to-all broadcast scenarios and put forward R-Code, a reliable and efficient broadcast protocol based on intra-flow network coding. The key idea is to construct a minimum spanning tree as a backbone whose link weight is the expected number of transmissions on that link. The broadcast overhead and delay are simultaneously reduced by enabling the non-leaf nodes in the tree to move to the next batch of file segments as early as possible, while ensuring their downstream nodes reliably receive and correctly decode all the packets in the current batch. Opportunistic overhearing is utilized to further reduce the number of transmissions. Extensive simulation results show that our scheme always achieves 100% packet delivery ratio (PDR), while enjoying less broadcast overhead and much shorter delay than AdapCode, 14% and 50%, respectively.

Abstract—Opportunistic Routing (OR) has been considered as one promising technique to overcome the unreliability of the wireless medium by collaborating multiple neighboring re-ceivers/candidates for packet forwarding. A key challenge in OR is how to efficiently coordinate the multiple candidates and ensure only one of them to forward the packet. In this paper, we investigate the existing candidate coordination schemes and propose a“fast slotted acknowledgment ” (FSA) to further improve the performance of OR by using single ACK with the help of channel sensing technique. The simulation results show that FSA can reduce the average end-to-end time delay of OR protocols by up to 50 % compared with state-of-the-art coordination schemes in light traffic scenarios and can increase the average end-to-end throughput by up to 20 % in heavy traffic scenarios. I.

Recently, distributed data storage has gained increasing popularity for efficient and robust data management in wireless sensor networks (WSNs). But the distributed architecture also makes it challenging to build a highly secure and dependable yet lightweight data storage system. On the one hand, sensor data are subject to not only Byzantine failures, but also dynamic pollution attacks, as along the time the adversary may modify/pollute the stored data by compromising individual sensors. On the other hand, the resource-constrained nature of WSNs precludes the applicability of heavyweight security designs. To address the challenges, we propose a novel dependable and secure data storage scheme with dynamic integrity assurance in this paper. Based on the principle of secret sharing and erasure coding, we first propose a hybrid share generation and distribution scheme to achieve reliable and fault-tolerant initial data storage by providing redundancy for original data components. To further dynamically ensure the integrity of the distributed data shares, we then propose an efficient data integrity verification scheme exploiting the technique of algebraic signatures. The proposed scheme enables individual sensors to verify in one protocol execution all the pertaining data shares simultaneously in the absence of the original data. Extensive security and performance analysis shows that the proposed schemes have strong resistance against various attacks and are practical for WSNs.

Distributed sensor data storage and retrieval has gained increasing popularity in recent years for supporting various applications. While distributed architecture enjoys a more robust and fault-tolerant wireless sensor network (WSN), such architecture also poses a number of security challenges especially when applied in mission-critical applications such as battle field and e-healthcare. First, as sensor data are stored and maintained by individual sensors and unattended sensors are easily subject to strong attacks such as physical compromise, it is significantly harder to ensure data security. Second, in many mission-critical applications, fine-grained data access control is a must as illegal access to the sensitive data may cause disastrous result and/or prohibited by the law. Last but not least, sensors usually are resource-scarce, which limits the direct adoption of expensive cryptographic primitives. To address the above challenges, we propose in this paper a distributed data access control scheme that is able to fulfill fine-grained access control over sensor data and is resilient against strong attacks such as sensor compromise and user colluding. The proposed scheme exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for WSNs with respect to both performance and security requirements. The feasibility of the scheme is demonstrated by experiments on real sensor platforms. To our best knowledge, this paper is the first to realize distributed fine-grained data access control for WSNs.

Cloud computing has been envisioned as the next-generation architecture of IT enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in the cloud, we propose an effective and flexible distributed scheme with two salient features, opposing to its predecessors. By utilizing the homomorphic token with distributed verification of erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server (s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.

Multi-hop broadcast is a key technique to disseminate important information such as time-sensitive safety warning messages (WMs) in Vehicular Ad hoc Networks (VANETs). Due to the fact that the implementation of broadcast at the link layer uses unreliable transmissions (i.e., lack of positive ACKs), highly reliable, scalable, and fast multi-hop broadcast protocol is particularly difficult to design in VANETs with unreliable links. Schemes that use redundant network layer broadcasts have been proposed. However, the balance between receiving reliability and transmission count in such schemes needs to be carefully considered. In this paper, we propose the opportunistic broadcast protocol (OppCast) that aims at minimizing the number of transmissions while achieving high network packet reception ratio (PRR) and fast multi-hop message propagation simultaneously. A double-phase broadcast strategy is proposed to achieve fast message propagation in one phase and to ensure high PRR in the other. The idea of opportunistic forwarding is exploited at each hop to minimize the propagation latency. An opportunistic forwarding protocol is designed accordingly as a MAC-layer broadcast coordination function, that allows multiple nodes to agree on the actual relay nodes in a distributed fashion. The proposed function also alleviates the hidden terminal problem. Theoretical analysis is carried out to optimize and design both broadcast phases. Extensive simulation results show that, compared with existing competing protocols, OppCast achieves close to 100% PRR and fast dissemination rate under a wide range of vehicle densities, while using significantly smaller number of transmissions.

Link quality measurement (LQM), i.e. packet reception ratio (PRR) measurement, is becoming an indispensable component in multihop wireless networks. However, in all the existing LQM mechanisms, a common fact is that a node's knowledge about the forward PRR from itself to its neighbor is informed by the neighbor. On the one hand, this receiver- dependent measurement provides accurate and timely updates on the link quality. On the other hand, it opens up a door for a malicious node to easily report a false measurement result to mislead the routing decision and degrade the system performance. In this paper, we analyze the security vulnerabilities in the existing LQM mechanisms and propose an efficient broadcast- based secure LQM (SLQM) mechanism, which prevents the malicious receiver from reporting a higher PRR than the actual one. We analyze the security strength and the cost of the proposed mechanism. Simulation results show that even when there are only 10% malicious nodes in the network, the average end-to-end throughput can be degraded by 50% compared with the normally operated network, which demonstrates the importance of employing SLQM mechanisms. To the best of our knowledge, this is the first work addressing the SLQM problem in multihop wireless networks.

Recently, multi-hop wireless mesh networks (WMNs) have attracted increasing attention and deployment as a low-cost approach to provide broadband Internet access at metropolitan scale. Security and privacy issues are of most concern in pushing the success of WMNs for their wide deployment and for supporting service-oriented applications. Despite the necessity, limited security research has been conducted towards privacy preservation in WMNs. This motivates us to develop PEACE, a soPhisticated privacy-Enhanced yet Accountable seCurity framEwork, tailored for WMNs. At the one hand, PEACE enforces strictuser access control to cope with both free riders and malicious users. On the other hand, PEACE offers sophisticated user privacy protection against both adversaries and various other network entities. PEACE is presented as a suite of authentication and key agreement protocols built upon our proposed short group signature variation. Our analysis shows that PEACE is resilient to a number of security and privacy related attacks.

Routing in multi-hop wireless networks presents a great challenge mainly due to unreliable wireless links and interference among concurrent transmissions. Recently, a new routing paradigm, opportunistic routing (OR), is proposed to cope with the unreliable transmissions by exploiting the broadcast nature and spatial diversity of the wireless medium. Previous studies on OR focused on networks with a single channel rate. The performance of OR in a multi-rate scenario is not carefully studied. In addition, although simulation and practical implementation have shown that OR achieves better throughput performance than that of traditional routing, there is no theoretical results on capacity enhancement provided by OR or network capacity bounds of OR. In this paper, we bridge these gaps by carrying out a comprehensive study on the impacts of multiple rates, interference, candidate selection and prioritization on the maximum end-to-end throughput or capacity of OR. Taking into consideration of wireless interference, we propose a new method of constructing transmission conflict graphs - we propose transmitter based conflict graph in contrast to link conflict graph. Then, we introduce the concept of concurrent transmitter sets to represent the constraints imposed by the transmission conflicts of OR, and formulate the maximum end-to-end throughput problem as a maximum-flow linear programming problem subject to the transmission conflict constraints. We also propose a rate selection scheme, and compare the throughput capacity of multi- rate OR with single-rate ones. We validate the analysis results by simulation, and show that OR has great potential to improve end- to-end throughput and system operating at multi-rates achieves higher throughput than that operating at any single rate.

Multi-hop broadcast is an important means to disseminate safety information like time-sensitive emergency messages (EMs) in Vehicular Ad hoc Networks (VANETs). Providing low-latency, high-coverage and scalable multi-hop EM broadcast is a hard task in VANET with unreliable links. The major challenge

Privacy-aware Public Key Infrastructure (PKI) can maintain user access control and yet protect user privacy, which is envisioned as a promising technique in many emerging applications. To justify the applicability of privacy-aware PKI and optimize the performance, it is highly important to ensure th

In many applications, it is desired to dynamically establish temporary multicast groups for secure message delivery. It is also often the case that the group membership information itself is sensitive and needs to be well protected. However, existing solutions either fail to address the issue of membership anonymity or do not scale well for dynamically established groups. In this paper, we propose a highly scalable solution for dynamical multicast group setup and yet protecting group membership anonymity simultaneously. In the proposed solution, scalability and membership anonymity are achieved via a novel design that integrates both ciphertextpolicy attribute-based encryption (CP-ABE) and centralized flat table (CFT) techniques. In our design, multicast groups are specified through group member attributes represented through binary member ID only and thus achieves scalability. Also, high level of membership anonymity is guaranteed such that every group member knows nothing but his own group membership only. The proposed solution is also efficient in communication, that is, the ciphertext size is only O(n), where n is the length of a group member ID and independent to the group size.

Popularity of group-oriented applications motivates research on security and privacy protection for group communications. A number of group key agreement protocols exploiting ID-based cryptosystem have been proposed for this objective. Though bearing beneficial features like reduced management cost, private key delegation from ID-based cryptosystem, they have not taken into account privacy issues during group communication. In wireless networks, the privacy problem becomes more crucial and urgent for mobile users due to the open nature of radio media. In this paper, we proposed an anonymous ID- based group key agreement protocol for wireless networks. Based on ID-based cryptosystem, our protocol not only benefits from the desirable features of ID-based cryptosystem, but also provides privacy protection for mobile users. More important, in the proposed protocol, the computation cost for each group member is largely reduced to meet the computation capability restriction of mobile devices.

Providing desirable data security, that is, confidentiality, authenticity, and availability, in wireless sensor networks (WSNs) is challenging, as a WSN usually consists of a large number of resource constraint sensor nodes that are generally deployed in unattended/hostile environments and, hence, are exposed to many types of severe insider attacks due to node compromise. Existing security designs mostly provide a hop-by-hop security paradigm and thus are vulnerable to such attacks. Furthermore, existing security designs are also vulnerable to many types of denial of service (DoS) attacks, such as report disruption attacks and selective forwarding attacks and thus put data availability at stake. In this paper, we seek to overcome these vulnerabilities for large-scale static WSNs. We come up with a location-aware end-to-end security framework in which secret keys are bound to geographic locations and each node stores a few keys based on its own location. This location-aware property effectively limits the impact of compromised nodes only to their vicinity without affecting end-to-end data security. The proposed multifunctional key management framework assures both node-to-sink and node-to-node authentication along the report forwarding routes. Moreover, the proposed data delivery approach guarantees efficient en-route bogus data filtering and is highly robust against DoS attacks. The evaluation demonstrates that the proposed design is highly resilient against an increasing number of compromised nodes and effective in energy savings.

Event boundary detection is in and of itself a useful application in wireless sensor networks (WSNs). Typically, it includes the detection of a large-scale spatial phenomenon such as the transportation front line of a contamination or the diagnosis of network health. In this paper, we present SEBD, a fully distributed and light-weight secure event boundary detection scheme, which implements secure and fault-tolerant detection of event boundaries in an adversarial environment. An efficient key establishment protocol is first proposed which establishes location based keys at each sensor node to secure the communications. The idea of location-based keys also effectively minimizes the impact of node compromise such that a compromised node cannot impersonate other nodes at locations other than where it is. Then a collaborative endorsement scheme is designed to allow multiple nodes collectively endorsing a valid boundary claim for increased resilience against node compromise. SEBD further develops an enhanced (nonparametric) statistical model that supports localized detection and shows a much better accuracy and fault tolerance property as compared to previous models. The security strength and performance of SEBD are evaluated by both analysis and simulations.

Opportunistic routing (OR) copes with the unreliable transmissions by exploiting the broadcast nature of the wireless medium and spatial diversity of the multi-hop wireless networks. In this paper, we carry out a comprehensive study on the impacts of multiple rates, interference, candidate selection and prioritization on the maximum end-to-end throughput or capacity of OR. Taking into account the wireless interference and unique properties of OR, we introduce the concept of concurrent transmitter sets to represent the constraints imposed by the transmission conflicts of OR, and formulate the maximum end-to-end throughput problem as a maximum-flow linear programming subject to the transmission conflict constraints. We also propose two multi-rate OR metrics: expected medium time (EMT) and expected advancement rate (EAR), and the corresponding distributed and local rate and candidate set selection schemes, one of which is least medium time OR (LMTOR) and the other is multi-rate geographic OR (MGOR). We compare the capacity of multi-rate OR with single-rate ones under different settings. We show that our proposed multi-rate OR schemes achieve higher throughput bound than any single-rate GOR. We observe some insights of OR: 1) although involving more forwarding candidates increases the end-to-end capacity, the capacity gained from involving more forwarding candidates decreases; 2) there exists a node density threshold, higher than which 24 Mbps GOR performs better than 12 Mbps GOR, and vice versa.

Geographic opportunistic routing (GOR) is a new routing concept in multihop wireless networks. In stead of picking one node to forward a packet to, GOR forwards a packet to a set of candidate nodes and one node is selected dynamically as the actual forwarder based on the instantaneous wireless channel condition and node position and availability at the time of transmission. GOR takes advantages of the spatial diversity and broadcast nature of wireless communications and is an efficient mechanism to combat the unreliable links. The existing GOR schemes typically involve as many as available next-hop neighbors into the local opportunistic forwarding, and give the nodes closer to the destination higher relay priorities. In this paper, we focus on realizing GOR's potential in maximizing throughput. We start with an insightful analysis of various factors and their impact on the throughput of GOR, and propose a local metric named expected one-hop throughput (EOT) to balance the tradeoff between the benefit (i.e., packet advancement and transmission reliability) and the cost (i.e., medium time delay). We identify an upper bound of EOT and proof its concavity. Based on the EOT, we also propose a local candidate selection and prioritization algorithm. Simulation results validate our analysis and show that the metric EOT leads to both higher one-hop and path throughput than the corresponding pure GOR and geographic routing.

Broadcast authentication is a critical security service in wireless sensor networks (WSNs), since it enables users to broadcast the WSN in an authenticated way. Symmetric key based schemes such as muTESLA and multilevel muTESLA have been proposed to provide such services for WSNs; however, these schemes all suffer from serious DoS attacks due to the delay in message authentication. This paper presents several effective public key based schemes to achieve immediate broadcast authentication and thus overcome the vulnerability presented in the muTESLA-like schemes. Several cryptographic techniques, including Merkle hash tree and identity-based signature scheme, are adopted to minimize the scheme overhead regarding the costs on both computation and communication. A quantitative energy consumption analysis of the proposed schemes is given in detail. We believe that this paper can serve as the start point towards fully solving the important multisender broadcast authentication problem in WSNs.

Event boundary detection is in and of itself a useful application in wireless sensor networks (WSNs). Typically, it includes the detection of a large-scale spatial phenomenon such as the transportation front line of a contamination or the diagnosis of network health. In this paper, we present FEBD, a fully distributed and light-weight fault-tolerant event boundary detection scheme. FEBD features an enhanced (nonparametric) statistical model that supports localized detection among neighboring nodes. To enhance detection accuracy, FEBD also introduces an error suppression technique prior to the determination of boundary nodes. The proposed scheme shows a much better detection accuracy and fault tolerance properties as compared to the previous models. The proposed FEBD is evaluated by extensive simulations, and presents very good detection accuracy, even when sensor fault probability is as high as 20%.

Providing desirable data security, that is, confidentiality, authenticity, and availability, in wireless sensor networks (WSNs) is challenging, as a WSN usually consists of a large number of resource constraint sensor nodes that are generally deployed in unattended/hostile environments and, hence, are exposed to many types of severe insider attacks due to node compromise. Existing security designs mostly provide a hop-by-hop security paradigm and thus are vulnerable to such attacks. Furthermore, existing security designs are also vulnerable to many types of Denial of Service (DoS) attacks, such as report disruption attacks and selective forwarding attacks and thus put data availability at stake. In this paper, we seek to overcome these vulnerabilities for large-scale static WSNs. We come up with a location-aware end-to-end security framework in which secret keys are bound to geographic locations and each node stores a few keys based on its own location. This location-aware property effectively limits the impact of compromised nodes only to their vicinity without affecting end-to-end data security. The proposed multifunctional key management framework assures both node-to-sink and node-to-node authentication along the report forwarding routes. Moreover, the proposed data delivery approach guarantees efficient en-route bogus data filtering and is highly robust against DoS attacks. The evaluation demonstrates that the proposed design is highly resilient against an increasing number of compromised nodes and effective in energy savings. Index Terms—Data security, wireless sensor network, end-to-end, DoS attack, false-data injection attack.

Wireless sensor networks are characterized by multihop wireless lossy links and resource constrained nodes. Energy efficiency is a major concern in such networks. In this paper, we study Geographic Routing with Environmental Energy Supply (GREES) and propose two protocols, GREES-L and GREES-M, which combine geographic routing and energy-aware routing techniques and take into account the realistic lossy wireless channel condition and the renewal capability of environmental energy supply when making routing decisions. Simulation results show that GREESs are more energy efficient than the corresponding residual energy based protocols and geographic routing protocols without energy awareness. GREESs can maintain higher mean residual energy on nodes, and achieve better load balancing in terms of having smaller standard deviation of residual energy on nodes. Both GREES-L and GREES-M exhibit graceful degradation on end-to-end delay, but do not compromise the end-to-end throughput performance.

Node compromise is a serious threat to wireless sensor networks deployed in unattended and hostile environments. To mitigate the impact of compromised nodes, we propose a suite of location-based compromise-tolerant security mechanisms. Based on a new cryptographic concept called pairing, we propose the notion of location-based keys (LBKs) by binding private keys of individual nodes to both their IDs and geographic locations. We then develop an LBK-based neighborhood authentication scheme to localize the impact of compromised nodes to their vicinity. We also present efficient approaches to establish a shared key between any two network nodes. In contrast to previous key establishment solutions, our approaches feature nearly perfect resilience to node compromise, low communication and computation overhead, low memory requirements, and high network scalability. Moreover, we demonstrate the efficacy of LBKs in counteracting several notorious attacks against sensor networks such as the Sybil attack, the identity replication attack, and wormhole and sinkhole attacks. Finally, we propose a location-based threshold-endorsement scheme, called LTE, to thwart the infamous bogus data injection attack, in which adversaries inject lots of bogus data into the network. The utility of LTE in achieving remarkable energy savings is validated by detailed performance evaluation.

This paper studies key management, a fundamental problem in securing mobile ad hoc networks (MANETs). We present IKM, an ID-based key management scheme as a novel combination of ID-based and threshold cryptography. IKM is a certificateless solution in that public keys of mobile nodes are directly derivable from their known IDs plus some common information. It thus eliminates the need for certificate-based authenticated public-key distribution indispensable in conventional public-key management schemes. IKM features a novel construction method of ID-based public/private keys, which not only ensures high-level tolerance to node compromise, but also enables efficient network-wide key update via a single broadcast message. We also provide general guidelines about how to choose the secret-sharing parameters used with threshold cryptography to meet desirable levels of security and robustness. The advantages of IKM over conventional certificate-based solutions are justified through extensive simulations. Since most MANET security mechanisms thus far involve the heavy use of certificates, we believe that our findings open a new avenue towards more effective and efficient security design for MANETs

The shared wireless medium of mobile ad hoc networks facilitates passive, adversarial eavesdropping on data communications whereby adversaries can launch various devastating attacks on the target network. To thwart passive eavesdropping and the resulting attacks, we propose a novel anonymous on-demand routing protocol, termed MASK, which can accomplish both MAC-layer and network-layer communications without disclosing real IDs of the participating nodes under a rather strong adversary model. MASK offers the anonymity of senders, receivers, and sender-receiver relationships in addition to node unlocatability and untrackability and end-to-end flow untraceability. It is also resistant to a wide range of attacks. Moreover, MASK preserves the high routing efficiency as compared to previous proposals. Detailed simulation studies have shown that MASK is highly effective and efficient.

Abstract In a wireless sensor network (WSN), pre‐distribution of secret keys is possibly the most practical approach to protect network communications. To meet the stringent resource constraints of the sensor nodes, key pre‐distribution schemes should be highly efficient, require as little storage space as possible, and at the same time, maintain a strong security strength, that is, high resilience against node capture. In this paper, a new approach for random key pre‐distribution is proposed to achieve both efficiency and security goals. The novelty of this approach lies in that, instead of using a key pool consisting of random keys, a key generation technique is carefully designed such that a large number of random keys can be represented by a small number of key‐generation keys. Then, instead of storing a big number of random keys, each sensor node stores a small number of key‐generation keys while computing the shared secret keys during the bootstrapping phase on the fly using the computationally efficient hash function. The proposed scheme outperforms the previous random key pre‐distribution schemes in that it reduces the storage requirement significantly while holding the comparable security strength, as shown by our thorough analysis and simulation. Copyright © 2006 John Wiley & Sons, Ltd.

Privacy and security are two important but seemingly contradict objectives in pervasive computing environments (PCEs). On the one hand, service providers want to authenticate service users and make sure they are accessing only authorized services in a legitimate way. On the other hand, users want to maintain necessary privacy without being tracked down for wherever they are and whatever they are doing. In this paper we propose a novel privacy enhanced authentication and access control scheme to secure the interactions between mobile users and services in PCEs. The proposed scheme seamlessly integrates two underlying cryptographic primitives, blind signature and hash chain, into a highly flexible and lightweight authentication and key establishment protocol. It provides explicit mutual authentication between a user and a service, while allowing the user to anonymously interact with the service. Differentiated service access control is also enabled in the proposed scheme by classifying mobile users into different service groups.

This paper studies public-key management, a fundamental problem in providing security support for mobile ad hoc networks. The infrastructureless nature and network dynamics of ad hoc networks make the conventional certificate-based public-key solutions less suitable. To tackle this problem, we propose a novel anonymous and certificateless public-key infrastructure (AC-PKI) for ad hoc networks. AC-PKI enables public-key services with certificateless public keys and thus avoids the complicated certificate management inevitable in conventional certificate-based solutions. To satisfy the demand for private keys during network operation, we employ the secret-sharing technique to distribute a system master-key among a preselected set of nodes, called D-PKG, which offer a collaborative private-key-generation service. In addition, we identify pinpoint attacks against D-PKG and propose anonymizing D-PKG as the countermeasure. Moreover, we determine the optimal secret-sharing parameters to achieve the maximum security.

Due to the broadcast nature of radio transmissions, communications in mobile ad hoc networks (MANETs) are more susceptible to malicious traffic analysis. In this paper we propose a novel anonymous on-demand routing protocol, termed MASK, to enable anonymous communications thereby thwarting possible traffic analysis attacks. Based on a new cryptographic concept called pairing, we first propose an anonymous neighborhood authentication protocol which allows neighboring nodes to authenticate each other without revealing their identities. Then utilizing the secret pairwise link identifiers and keys established between neighbors during the neighborhood authentication process, MASK fulfills the routing and packet forwarding tasks nicely without disclosing the identities of participating nodes under a rather strong adversarial model. MASK provides the desirable sender and receiver anonymity, as well as the relationship anonymity of the sender and receiver. It is also resistant to a wide range of adversarial attacks. Moreover, MASK preserves the routing efficiency in contrast to previous proposals. Detailed anonymity analysis and simulation studies are carried out to validate and justify the effectiveness of MASK.

A typical task in a wireless sensor network is that every sensor node senses its local environment and, upon request, sends the data of interest back to a base station. Based on this many-to-one communication pattern, we first propose a distributed N-to-1 multipath discovery protocol which distinguishes from other multipath routing protocols in that it is able to find multiple node-disjoint paths from every sensor node to the base station simultaneously in one route discovery process. Then we propose a hybrid multipath data collection scheme which combines end-to-end multipath traffic dispersion and per-hop alternate path salvaging. Our simulation results show that the proposed N-to-1 multipath discovery protocol is highly efficient and the hybrid data collection scheme based on it provides a seamlessly more reliable and more secure data collection service in wireless sensor networks.

Wireless sensor networks are often deployed in unattended and hostile environments, leaving individual sensors vulnerable to security compromise. The paper proposes the novel notion of location-based keys for designing compromise-tolerant security mechanisms for sensor networks. Based on location-based keys, we develop a node-to-node authentication scheme, which is able not only to localize the impact of compromised nodes within their vicinity, but also to facilitate the establishment of pairwise keys between neighboring nodes. Compared with previous proposals, our scheme has perfect resilience against node compromise, low storage overhead, and good network scalability. We also demonstrate the use of location-based keys in combating a few notorious attacks against sensor network routing protocols.

Wireless sensor networks (WSNs) are appealing in obtaining fine-granular observations about the physical world. Due to the fact that WSNs are composed of a large number of low-cost but energy-constrained sensor nodes, along with the notorious time-varying and error-prone nature of wireless links, scalable, robust, and energy-efficient data disseminating techniques are requisite for the emerging WSN applications such as environment monitoring and surveillance. To meet this challenging demand, we propose a hybrid data dissemination framework for WSNs in this paper. In particular, we conceptually partition a whole sensor field into several functional regions and apply different routing schemes to different regions in order to provide better performance in terms of reliability and fair energy usage. For this purpose, we also propose a novel zone flooding scheme, essentially a combination of geometric routing and flooding techniques. Our scheme features low overhead, high reliability, good scalability, and notable flexibility. Simulation studies are carried out to validate the effectiveness and efficiency of our scheme.

Security is a critical issue in a mobile ad hoc network (MANET). We propose and investigate a novel scheme, security protocol for reliable data delivery (SPREAD), to enhance the data confidentiality service in a mobile ad hoc network. The proposed SPREAD scheme aims to provide further protection to secret messages from being compromised (or eavesdropped) when they are delivered across the insecure network. The basic idea is to transform a secret message into multiple shares by secret sharing schemes and then deliver the shares via multiple independent paths to the destination so that even if a small number of nodes that are used to relay the message shares are compromised, the secret message as a whole is not compromised. We present the overall system architecture and investigate the major design issues. We first describe how to obtain message shares using the secret sharing schemes. Then we study the appropriate choice of the secret sharing schemes and the optimal allocation of the message shares onto each path in order to maximize the security. The results show that the SPREAD is more secure and also provides a certain degree of reliability without sacrificing the security. Thirdly, the multipath routing techniques are discussed and the path set optimization algorithm is developed to find the multiple paths with the desired property, i.e., the overall path set providing maximum security. Finally, we present the simulation results to justify the feasibility and evaluate the effectiveness of SPREAD.

Wireless sensor networks (WSNs) are appealing in obtaining fine-granular observations about the physical world. Due to the fact that WSNs are composed of a large number of low-cost but energy-constrained sensor nodes, along with the notorious timer-varying and error-prone natures of wireless links, scalable, robust, and energy-efficient data disseminating techniques are requisite for the emerging WSN applications such as environment monitoring and surveillance. In this paper we examine this emerging field from a view of supply chain management and propose a hybrid data dissemination framework for WSNs. In particular, we conceptually partition a whole sensor field into several functional regions based on the supply chain management methodology, and apply different routing schemes to different regions in order to provide better performance in terms of reliability and energy usage. For this purpose, we also propose a novel zone flooding scheme, essentially a combination of geometric routing and flooding techniques. Our hybrid data dissemination framework features low overhead, high reliability, good scalability and flexibility, and preferable energy efficiency. Detailed simulation studies are carried out to validate the effectiveness and efficiency of our scheme.

Security in mobile ad hoc networks (MANETs) has received intensive attention recently, whereas the issue of selfish nodes, which may refuse to forward packets for others to save their own resources, is not well addressed yet. This kind of noncooperative action would cause a severe problem that is more likely in MANETs compared to their wired counterpart To cope with this problem, we propose SIP: a secure incentive protocol to stimulate cooperation among those possible selfish nodes. The most attractive feature of SIP is that it does not rely on any predeployed infrastructure and provides highly secure incentives for selfish nodes to be cooperative in packet forwarding with low overhead and implementation complexity.

We propose a novel media access control (MAC) architecture to support differentiated service (DiffServ) in IEEE 802.11 WLAN. By employing the MAC-core as base and different adaptors as add-ons in this architecture, the resulting MAC can provide prioritized services with different delays and throughputs. Our simulation studies show that the resulting MAC protocols based on this architecture can achieve low access delay and high throughput for high priority traffic while maintaining fairness.

It is well-known that distributed delay-constrained least-cost (DCLC) unicast routing problem is NP-complete. In this paper we propose an efficient distributed algorithm, namely, selection function based DCLC (SF-DCLC), based on a novel selection function for the DCLC problem. The proposed SF-DCLC algorithm requires limited network state information at each network node and is always able to find a loop-free path satisfying the delay bound if such paths exist. Simulation study show that the SF-DCLC is not as sensitive to the delay bound and network size as some other DCLC routing algorithms, and attains very low cost-inefficiency (less than 3% to the optimal One) in various network scenarios we simulate. The most attractive feature of SF-DCLC is that SF-DCLC has very high probability to find the optimal solution or a near-optimal solution in polynomial time with low computational complexity and message complexity.