The Industrial Internet of Things (IIoT) has been widely deployed to enable real-time monitoring and automation. Within IIoT-driven production, supply chain management plays a critical role, necessitating verifiable provenance to ensure the authenticity and traceability of goods across multi-stakeholder networks. While blockchain provides a tamper-proof foundation, traditional storage structures suffer from unsecured data integrity, poor query efficiency, and scalability over provenance data. To address these challenges, we propose vProChain, an efficient provenance verification system to support verifiable and parallel queries over graph-structured provenance data. First, we design an Adaptive DAG Verkle Tree (ADVT) that deterministically maps supply chain dependencies into a graph-native authenticated data structure, enabling constant-size proofs and low-overhead verification. Second, we introduce the Merkle Inverted Patricia Trie (MIPT) to facilitate fast, verifiable multi-dimensional Boolean queries. Third, we develop a parallel provenance query algorithm that accelerates multi-hop path retrieval via consistent hashing and weighted bipartite matching. Finally, formal security analysis and extensive empirical evaluations demonstrate that vProChain can provide provable cryptographic guarantees for the soundness of provenance proofs and the completeness of query retrievals, while achieving high query efficiency in a large-scale IIoT environment.

With the growing adoption of electronic voting in digital societies, ensuring voter anonymity and ballot integrity is essential for secure and trustworthy elections. Blind ring signature is a promising cryptographic primitive that simultaneously provides blindness (concealing the link between signer and message) and ring anonymity (hiding the actual signer within a group), thus enabling anonymous yet verifiable voting. However, most existing schemes either rely on discrete logarithm assumptions, rendering them vulnerable to quantum adversaries, or fail to achieve both blindness and ring anonymity within a single construction. In the post quantum setting, only a handful of lattice-based blind ring signature schemes have been proposed, and all suffer from prohibitively large signature sizes, limiting their practicality for large-scale deployments. Consequently, there is a pressing need for lattice-based blind ring signatures that achieve post-quantum security, simultaneous blindness and ring anonymity, and practical efficiency for large-scale elections. In this work, we present LBRS, the first lattice-based blind ring signature scheme that transforms the two-round SnowBlind protocol [Crypto'23] from the discrete logarithm setting to the lattice setting while seamlessly integrating ring signature functionality. Unlike existing solutions, our design achieves post-quantum security while preserving blindness and introducing anonymity, making it robust against quantum attackers and capable of simultaneously providing blindness and ring anonymity. We implement LBRS within a complete post-quantum anonymous voting architecture, ensuring end-to-end security and full voter anonymity, while maintaining ballot integrity, enabling transparent and verifiable tallying, and supporting practical deployment. We formally prove that LBRS satisfies correctness, anonymity, blindness, and one-more unforgeability under standard lattice assumptions. Experiments show that LBRS reduces blind signature sizes to 58.37% and real signature sizes to 2.75%-16.52% of those in prior arts, while significantly decreasing registration time and maintaining comparable costs in other phases. These advantages scale with ring size, making LBRS a strong candidate for large-scale, post-quantum secure elections.

Web data management has become crucial to data sharing among users and servers. One promising approach to guaranteeing the privacy of shared data is searchable encryption (SE), which allows users to outsource encrypted data to the web server, which can then respond confidentially to keyword queries. Several SE schemes support access control to meet data-sharing requirements. However, several works (e.g., Zhang TSC'23, Zhang TCC'21) only focus on single-user access control and ignore the need for multi-user scenarios. Besides, a malicious data owner may send useless ciphertexts to the web server, potentially making the system insecure and impractical (e.g., Wang TPDS'22, Xu TDSC'20). As a result, research regarding owner authentication and multiuser access control in SE schemes remains underexplored. In this work, we construct SEOMA, the first multi-keyword encrypted search primitive supporting owner authentication and multi-user access control for Web data management. Unlike existing solutions, our design achieves owner authentication and multi-user access control simultaneously in a malicious setting. We incorporate attribute encryption to realize the attribute authentication for a data owner. Then, we leverage the policy tree and linear secret-sharing techniques to achieve hierarchical access control for users. We also formalize and demonstrate its security in a random oracle model by reducing to the DBDH and CBDH problem. Eventually, we conduct comprehensive performance evaluations compared to existing state-of-the-art schemes. Specifically, the computation and communication overhead is only 0.05-0.4× and 0.07-0.47× compared to prior arts, respectively.

The growing demand for computing power drives more artificial intelligence (AI) model owners to outsource their models to public clouds, relying on cloud servers to manage which users can use, train, or upgrade AI models. Unfortunately, existing work cannot simultaneously manage the entire model lifecycle in public clouds when considering model stealing attacks, where cloud servers covertly replicate AI models and deliver AI services to unauthorized users for profit. As a result, model owners are forced to conduct training and upgrades in private environments before deploying models to clouds for service delivery, which poses significant challenges in collaboration and maintenance, particularly for models requiring frequent upgrades. In this paper, we introduce$\mathsf {Trident}$, the first secure cloud-based framework for flexible AI model lifecycle management, including availability, trainability, and upgradability. By leveraging multiple cryptographic techniques, such as access control trees,$\mathsf {Trident}$ensures that AI models and their management policies are tightly coupled, compelling cloud servers to execute only specified model operations without violating management policies, thereby resisting model stealing attacks. Rather than straightforward cryptographic applications, we address a series of technical challenges, including shifting the focus of access control trees from data to model management and maintaining downward-compatible model management rights. We propose two detailed constructions: Semi-$\mathsf {Trident}$and Full-$\mathsf {Trident}$, tailored for semi-delegation and full-delegation scenarios, i.e., whether model owners need to interact with cloud servers while delivering AI services. Theoretical complexity analysis and security analysis prove the competitive efficiency and security. Experimental results show that compared to assembling existing partial-function schemes, Semi-$\mathsf {Trident}$and Full-$\mathsf {Trident}$achieve around$3.6\times$improvement in time costs and$5\times$improvement in communication overhead.

The growing deployment of Large Vision-Language Models (VLMs) raises safety concerns, as adversaries may exploit model vulnerabilities to induce harmful outputs, with targeted black-box adversarial attacks posing a particularly severe threat. However, existing methods primarily maximize encoder-level global similarity, which lacks the granularity for stealthy and practical fine-grained attacks, where only specific target should be altered (e.g., modifying a car while preserving its background). Moreover, they largely neglect the projector, a key semantic bridge in VLMs for multimodal alignment. To address these limitations, we propose a novel black-box targeted attack framework that leverages the projector. Specifically, we utilize the widely adopted Querying Transformer (Q-Former) which transforms global image embeddings into fine-grained query outputs, to enhance attack effectiveness and granularity. For global targeted attack scenarios, we propose the Intermediate Projector Guided Attack (IPGA), which aligns the fine-grained query outputs from Q-Former with the target to enhance attack strength and exploits the intermediate pretrained Q-Former that is not fine-tuned for any specific Large Language Model (LLM) to improve transferability. For fine-grained attack scenarios, we augment IPGA with the Residual Query Alignment (RQA), which preserves unrelated content by constraining non-target query outputs, enhancing attack granularity. Extensive experiments demonstrate that IPGA significantly outperforms baselines in global targeted attacks, and IPGA with RQA (IPGA-R) attains superior success rates and content preservation over baselines in fine-grained attacks. Our method also transfers effectively to commercial VLMs such as Google Gemini and OpenAI GPT.

Adversarial examples generated on a surrogate deep neural network (DNN) can often successfully fool other black-box DNN models. This cross-model transferability poses serious security threats to DNNs in practical applications. Input transformation techniques are widely used to enhance adversarial transferability by increasing the diversity of input images. However, existing methods primarily rely on local operations with limited degrees of freedom (DOF), such as block-wise shuffling and resizing, overlooking global perspective transformations that naturally arise from viewpoint changes. In this work, we propose a Perspective-Invariant Attack (PIA), which introduces a multi-DOF vertex sampling strategy that systematically covers the perspective transformation hierarchy from 2-DOF translation to 8-DOF projective mapping. By generating geometrically diverse input variations, PIA effectively reduces overfitting of adversarial perturbations to the surrogate model, thereby improving adversarial transferability. We further propose PIA-Mix, a generic extension that maintains a complementary transformation pool and efficiently combines our perspective transformation with auxiliary methods for improved transferability. Extensive experiments involving various DNN architectures, advanced defense mechanisms, and multimodal large language models (LLMs) demonstrate that PIA and PIA-Mix outperform state-of-the-art transfer-based attacks.

Decentralized identity systems have emerged as a transformative paradigm, granting users unprecedented data sovereignty and privacy-preserving capabilities, fueling critical innovations in Web3 ecosystems. However, these systems primarily serve as identity-layer solutions, forcing verifiers to design special cryptographic protocols for access control deployment, which is an error-prone and expert-dependent process. Moreover, existing approaches fail to effectively combat credential fraud (e.g., credential theft and revoked credential reuse) without compromising privacy guarantees. This paper presents FRAC (Flexible Fraud-Resistant Access Control), an efficient decentralized access control framework that achieves two paradigm shifts: 1) Streamlined access control deployment: a logic-centric paradigm encodes access criteria through declarative verification rules, eliminating manual cryptographic protocol design while enabling instant verifier onboarding and efficient presentation generation; 2) Provable fraud resistance: a format-agnostic defensive mechanism based on Merkle trees prevents malicious credential use, requiring only lightweight hash operations and signature verification instead of computation-intensive operations. We conduct rigorous security analysis based on universally composable security and evaluate the performance, demonstrating FRAC’s security and efficiency.

Nowadays, mobile cloud storage has become increasingly prevalent for processing mobile data due to its convenience and resources. Towards the right to restriction of processing in current data regulations, some solutions have been proposed to empower data owners with identity-based hierarchical control for equality, comparison, and plaintext analytics operations. However, existing solutions either rely on specific hardware environments (i.e., trusted execution environments) or face two significant issues: identity privacy breaches, where attackers can identify targeted users, and excessive overhead in multi-user scenarios, as each user requires a distinct ciphertext. In this paper, we leverage multiple cryptographic primitives to introduce PODS, an efficient and secure hierarchical data processing control scheme for multi-user mobile cloud storage. PODS allows the data owner to generate a single ciphertext for multiple users without hardware reliance. Technically, we reconstruct identity-based encryption by leveraging well-designed common and private parameters, thereby shifting the focus from data itself to data processing operations. Then, we encode multiple targeted identities as polynomial coefficients and integrate these coefficients into identity-based data processing control. We achieve threefold benefits, effectively reducing overhead, hierarchically processing users' rights, and concealing the identities of targeted users to protect privacy. Security analysis proves the security of PODS. Experiments demonstrate that PODS achieves around$8\times$and$37\times$improvement in computation and communication compared to existing related works.

Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferable targeted attacks that can mislead DNN models into predicting specific target classes. While various methods have been proposed to enhance attack transferability, they often incur substantial computational costs while yielding limited improvements. Recent clean feature mixup methods use random clean features to perturb the feature space but lack optimization for disrupting adversarial examples, overlooking the advantages of attack-specific perturbations. In this paper, we propose Feature Tuning Mixup (FTM), a novel method that enhances targeted attack transferability by combining both random and optimized noises in the feature space. FTM introduces learnable feature perturbations and employs an efficient stochastic update strategy for optimization. These learnable perturbations facilitate the generation of more robust adversarial examples with improved transferability. We further demonstrate that attack performance can be enhanced through an ensemble of multiple FTM-perturbed surrogate models. Extensive experiments on the ImageNet-compatible dataset across various DNN models demonstrate that our method achieves significant improvements over state-of-the-art methods while maintaining low computational cost.1

Artificial intelligence has demonstrated significant potential for traffic analysis in IoT intrusion detection systems. However, existing machine learning (ML) solutions struggle with high false alarm rates due to a lack of malicious data. The advantages of large language models (LLMs), particularly their few-shot learning capabilities, can effectively address this issue. Nonetheless, LLMs face challenges such as high computational overhead and detection latency, which make them impractical for IoT intrusion detection. One promising solution is to leverage knowledge distillation, shrinking the LLM, the teacher model, into a smaller student model that requires limited malicious examples while preserving the low latency characteristic of traditional ML-based models. In this paper, we propose DLM-IDS, an efficient and accurate traffic analysis framework for IoT intrusion detection with small training datasets, empowered by LLM knowledge distillation. Specifically, DLM-IDS introduces a chain-of-thought (CoT) mechanism that enables a pre-trained LLM to interpret network traffic patterns without requiring additional training or fine-tuning. By extracting rationales from the LLM (around 200B parameters), the teacher model, DLM-IDS constructs a small yet highly accurate student traffic analysis model (around 200 million parameters). Experiments show that with only 800 examples in the training dataset, DLM-IDS maintains over 98% AUC while reducing inference time from 3.2s to 0.037s per flow detection, enabling practical low-latency deployment on resource-constrained IoT devices.

Recent works have attacked person detectors using adversarial patches or static-3D-model-based texture modifications. However, these methods suffer from low attack success rates when faced with significant human movements. The primary challenge stems from the highly non-rigid nature of the human body and clothing. Current attacks fail to model these 3D non-rigid deformations caused by varied actions. Fortunately, recent research has shown significant progress in using NeRF for dynamic human modeling. In this paper, we introduce \texttt{UV-Attack}, a novel physical adversarial attack achieving high attack success rates in scenarios involving extensive and unseen actions. We address the challenges above by leveraging dynamic-NeRF-based UV mapping. Our method can generate human images across diverse actions and viewpoints and even create novel unseen actions by sampling from the SMPL parameter space. While dynamic NeRF models are capable of modeling human bodies, modifying their clothing textures is challenging due to the texture being embedded within neural network parameters. To overcome this, \texttt{UV-Attack} generates UV maps instead of RGB images and modifies the texture stacks. This approach enables real-time texture edits and makes attacks more practical. Finally, we propose a novel Expectation over Pose Transformation loss (EoPT) to improve the evasion success rate on unseen poses and views. Our experiments show that \texttt{UV-Attack} achieves a 92.7\% attack success rate against the FastRCNN model across varied poses in dynamic video settings, significantly outperforming the state-of-the-art AdvCaT attack, which only had a 28.5\% ASR. Moreover, we achieve 49.5\% ASR on the latest YOLOv8 detector in black-box settings. The code is available at https://github.com/PolyLiYJ/UV-Attack

Nowadays, checkpoints have been proven to be an effective solution to ensure slashability in proof-of-stake (PoS) consensus, and Tas et al.'s cutting-edge solution in S&P 2023 is a typical example. Unfortunately, despite progress, hour-level transaction delays and annually around 10 K dollar checkpoint costs make existing related solutions still unacceptable in realworld PoS applications. In this paper, we propose SecPoS, a slashable PoS consensus with second-level transaction delays and one-time checkpoint costs. To achieve these design goals, we draw inspiration from Pixel+ signatures and chameleon hash functions to design a novel bilateral blockchain structure, achieving twoblock transaction finalization via only uploading the first block of our chain as checkpoints. Next, considering practical application requirements, we address a series of following challenges, such as bilateral immutability, blockchain forks, determination of the main chain, and malicious attacks from PoS members. In detail, we propose two constructions of SecPoS, i.e., SecPoS – A and SecPoS – B. SecPoS – A and SecPoS – B have a tradeoff between transaction delays and block numbers packed in an epoch. Compatible with most existing one-way blockchains, we implement and outsource a prototype SecPoS to facilitate research11https://github.com/Academic-Paper-Codes/SecPoS-Consensus, and prove the security of SecPoS. Experiments on this prototype show that SecPoS – A and SecPoS – B require around 5s and 100s transaction delays, respectively, and both require 2 dollars one-time checkpoint costs.

Recently, text-to-image diffusion models have been widely used for style mimicry and personalized customization through methods such as DreamBooth and Textual Inversion. This has raised concerns about intellectual property protection and the generation of deceptive content. Recent studies, such as Glaze and Anti-DreamBooth, have proposed using adversarial noise to protect images from these attacks. However, recent purification-based methods, such as DiffPure and Noise Upscaling, have successfully attacked these latest defenses, showing the vulnerabilities of these methods. Moreover, present methods show limited transferability across models, making them less effective against unknown text-to-image models. To address these issues, we propose a novel anti-mimicry method, StyleGuard. We propose a novel style loss that optimizes the style-related features in the latent space to make it deviate from the original image, which improves model-agnostic transferability. Additionally, to enhance the perturbation's ability to bypass diffusion-based purification, we designed a novel upscale loss that involves ensemble purifiers and upscalers during training. Extensive experiments on the WikiArt and CelebA datasets demonstrate that StyleGuard outperforms existing methods in robustness against various transformations and purifications, effectively countering style mimicry in various models. Moreover, StyleGuard is effective on different style mimicry methods, including DreamBooth and Textual Inversion. The code is available at \url{https://github.com/PolyLiYJ/StyleGuard}.

Large vision-language models (VLLMs) have driven significant progress in multi-modal systems, enabling a wide range of applications across domains such as healthcare, education, and content generation. Despite the success, the large-scale datasets used to train these models often contain sensitive or personally identifiable information, raising serious privacy concerns. To audit and better understand such risks, membership inference attacks (MIAs) have become a key tool. However, existing MIAs against VLLMs predominantly assume access to full-model logits, which are typically unavailable in many practical deployments. To facilitate MIAs in a more realistic and restrictive setting, we propose a novel framework: label-only membership inference attacks (LOMIA) targeting pre-trained VLLMs where only the model’s top-1 prediction is available. Within this framework, we propose three effective attack methods, all of which exploit the intuition that training samples are more likely to be memorized by the VLLMs, resulting in outputs that exhibit higher semantic alignment and lower perplexity. Our experiments show that our framework surpasses existing label-only attack adaptations for different VLLMs and competes with state-of-the-art logits-based attacks across all metrics on three widely used open-source VLLMs and GPT-4o.

Driven by information technology, data trading promotes cross-industry collaboration and uncovers value by integrating multi-source data, yet it requires encryption and access controls to address increasing data security challenges. Existing schemes largely rely on attribute-based unilateral access control to protect data, facing challenges such as data source authenticity and requester autonomy. Bilateral access control requires data providers and requesters to define access policies, allowing decryption only when both policies match, often facilitated by cryptographic primitives such as matchmaking encryption (ME). However, the current bilateral schemes still face challenges of sensitive data leakage, unauthorized data access, and single points of failure. To date, no existing scheme has addressed these issues simultaneously. In this paper, we propose a blockchain-based, sanitizable and bilateral access control scheme with privacy-preserving (SBAC-PP) for data trading. Specifically, by extending ME via hash functions and policy-hidden identifiers to achieve a bilateral access control with privacy-preserving. Secondly, by combining access control encryption (ACE), we design a ciphertext sanitization mechanism to prevent unauthorized data access. Furthermore, by integrating SBAC-PP with blockchain (BC) and the interplanetary file system (IPFS), we use smart contracts for trusted matching and pre-decryption, and store encrypted data in IPFS, thereby achieving decentralized data management to avoid single points of failure. Finally, we analyze the security of SBAC-PP and evaluate its performance to demonstrate its efficiency and practicality.

Continuous authentication (CA) has been widely applied by network service providers to verify user identities in finance, healthcare, and e-commerce fields. However, in next-generation networks, CA faces the risk of user privacy leakage due to its dependence on a verifier-centric authentication model, and verifiers may not always be trustworthy, particularly in zero-trust architecture networks. Existing privacy protection schemes face challenges in solving this problem because these schemes will weaken the linkability of context requests, leading to difficulties in consistency checks for fine-grained CA. To fill the gap, this article proposes a privacy-preserving CA (PPCA) scheme by incorporating anonymous self-sovereign identity and fine-grained CA. Specifically, PPCA exploits subset proof to enable users to reveal only the minimum necessary identity data for selective disclosure. To support fine-grained CA, we construct a new consistency proof for the anonymous user to prove that the different credentials are bound to the same attributes set, where the user is responsible for deciding whether to send the consistency proof. PPCA is formalized, defined, and constructed based on BLS signatures, Set Commitment, and Sigma Protocol. The security analysis shows that PPCA is correct and sound and supports user anonymity and credential consistency at the same time. The performance evaluation shows that PPCA requires only minimal additional time cost, achieving an optimal balance between security and efficiency.

The manufacturing environment and activities with a large volume and variety of product data have put forward higher requirements for the proof and verification of identity information. Achieving decentralized digital identity management in the Industrial Internet of Things (IIoT) helps to improve the performance of relevant proofs and authentication. The Decentralized Identity (DID) system serves as a bridge between the physical and digital worlds, assigning digital identities to physical entities to facilitate their participation in online activities. However, faced with the huge number of manufacturing entities accessing the DID system, the number of DID documents in the system has proliferated. It is still a big challenge to improve the scalability of the system while ensuring the efficiency of information access and verification. In this paper, we propose a blockchain-based verifiable decentralized identity system for IIoT. First, we propose a blockchain-based system architecture with a specially designed storage structure for DID documents. Specifically, we design a structure based on Merkle Tree that visually summarises the physical associations of manufacturing entities and reduces access overhead. Second, we design a multiblock storage structure within the blockchain, which establishes inter-block jumps based on the associated DID, effectively improving the query efficiency of the system. Finally, we design a verification scheme that enables users to verify the integrity of the identity data of the proof provider. We implemented the system framework and conducted experiments to evaluate the performance of our system. The experimental results proved the effectiveness of the system.

Prediction queries have revolutionized data search by integrating machine learning models and traditional data processing operations for advanced analytics. However, existing prediction query frameworks for outsourced databases face a critical security vulnerability: data flows are processed in plaintext on semi-honest servers, making them susceptible to data breaches. The main challenge in achieving secure prediction queries is that machine learning inference and data processing operations are distinct functionalities, while most current cryptographic frameworks support only a single type of operation on specific encrypted data. To bridge this crucial gap, we propose$\mathsf {SecPQ}$, the first framework tailored for secure prediction queries. Our approach unifies decision tree pipelines and data processing operations, such as selection, projection, and equality-joining, through equality matching on encrypted outsourced data. This enables the design of secure prediction queries with decision tree pipelines operating on encrypted data. We provide formal security definitions and proofs for$\mathsf {SecPQ}$. To further optimize the efficiency of secure prediction queries, we leverage order-preserving encryption to construct$\mathsf {SecPQ}_{{{ope}}}$, which offers improved query efficiency at the expense of weaker security properties compared with$\mathsf {SecPQ}$. Extensive experimental evaluations on billions of records demonstrate the feasibility and effectiveness of both$\mathsf {SecPQ}$and$\mathsf {SecPQ}_{{{ope}}}$.

Public Key Infrastructure (PKI) has gained widespread attention for ensuring the security and integrity of data communication. While existing PKI mainly supports digital signatures, it is lacking in crucial anonymity, leading to the leakage of a signer’s identity information. To alleviate the issue, ring signatures are a suitable choice to provide anonymity as they allow users to create their own rings without the need for an administrator. Unfortunately, the utilization of ring signatures in PKI may present compatibility challenges within the system. Thus, proposing a general mechanism to convert a standardized$\Sigma $-based signature to a ring signature is far-reaching. In this paper, we propose a general construction for converting$\Sigma $-based signatures into ring signatures. To achieve this, we first introduce a$\Sigma $-based general model, providing a general transformation to convert existing$\Sigma $-based signatures into a$\Sigma $-protocol form. Subsequently, we incorporate our redesigned one-out-of-many relation within our general model and proceed to devise ring signatures leveraging on one-out-of-many proofs. Furthermore, to reduce the signature size, we employ the Bulletproofs folding technique, enabling the attainment of logarithmic size ring signatures. To demonstrate the wide applicability of our general construction, we present four prominent signatures as case studies. Ultimately, we conduct a rigorous security analysis and benchmark experimental evaluation. The signing and verification times are 0.44 to 0.97 times and 0.27 to 0.91 times compared to other state-of-the-art schemes, respectively. Additionally, we exhibit the lowest signature size to date.

Vertical Federated Learning(VFL) has gained widespread attention due to its ability of enabling collaborative model training among participants with diverse data features.Differential Privacy(DP) offers provable privacy guarantees for VFL, but existing DP-based methods typically compromise accuracy for privacy protection. To address this issue, we propose a novel scheme, called Adaptive Differential Privacy-based Vertical Federated Learning (Ada-VFed), that enhances privacy of data features and labels by adding Gaussian noise separately to the transmitted intermediate results and gradients. To improve model accuracy, we incorporate adaptive constraints through regularization terms in the objective function to mitigate the impact of clipping operations. In addition, we propose a dynamic noise injection mechanism that adjusts noise according to the importance of each dimension, thereby balancing privacy protection and model accuracy. Our theoretical analysis provides privacy guarantees and convergence insights. Extensive experiments demonstrated that our scheme significantly outperforms state-of-the-art DP-based VFL methods in terms of accuracy. Even with a small privacy budget (e.g.,ϵ = 0.5), our method improves the accuracy on MNIST, FashionMNIST, and CIFAR-10 by 13.01%, 10.08%, and 3.40%, respectively, compared to traditional DP-based VFL methods.

The Internet of Medical Things (IoMT) has gained significant research focus in both academic and medical institutions. Nevertheless, the sensitive data involved in IoMT raises concerns regarding user validation and data privacy. To address these concerns, certificateless signcryption (CLSC) has emerged as a promising solution, offering authenticity, confidentiality, and unforgeability. Unfortunately, most existing CLSC schemes are impractical for IoMT due to their heavy computational and storage requirements. Additionally, these schemes are vulnerable to quantum computing attacks. Therefore, research focusing on designing an efficient post-quantum CLSC scheme is still far-reaching. In this work, we propose PQ-CLSCL, a novel post-quantum CLSC scheme with linkability for IoMT. Our proposed design facilitates secure transmission of medical data between physicians and patients, effectively validating user legitimacy and minimizing the risk of private information leakage. To achieve this, we leverage lattice sampling algorithms and hash functions to generate the partial secret key, then employ the sign-then-encrypt method and design a link label. We also formalize and prove the security of our design, including indistinguishability against chosen-ciphertext attacks (IND-CCA2), existential unforgeability against chosen-message attacks (EU-CMA), and linkability. Finally, through comprehensive performance evaluation, our computation overhead is just 5% of other existing schemes. The evaluation results demonstrate that our solution is practical and efficient.

Spatial keyword query has emerged as a critical service in mobile cloud, enabling cloud servers to retrieve spatiotextual objects within a mobile user's query range that contain specified query keywords. Numerous secure spatial keyword query schemes have been developed to enable geometric range queries and keyword searches on encrypted spatial data. However, spatial keyword queries are typically designed for searching high-dimensional spatial data across arbitrary geographic ranges. Most of them fail to handle arbitrary geometric range queries and efficient spatial keyword query over high-dimensional encrypted data. To address these issues, we propose a high-dimEnsional and Privacy-preserving Spatial Keyword Query (EPSKQ) scheme with arbitrary geometric ranges over encrypted spatial data, leveraging Hilbert curve encoding and Enhanced Matrix-based Inner Product Encryption (EMIPE). In EPSKQ, spatial locations and multi-keywords are encoded into compact vectors, and arbitrary geometric range queries are transformed into range intersection tests. To reduce computational overhead, we employ vector bucketing technique to partition large-size vectors into several small-size sub-vectors. Furthermore, we design a novel index structure called Hilbert Binary tree (HB-tree) to optimize range intersection tests. Based on HB-tree, we propose an enhanced spatial keyword query scheme, named EPSKQ+, which further improves query performance. Security analysis demonstrates that both EPSKQ and EPSKQ+ achieve semantic security against indistinguishability under chosen-plaintext attack (INDCPA). Extensive experimental evaluations show that the proposed EPSKQ and EPSKQ+ schemes significantly outperform state-ofthe-art schemes in terms of computational and communication costs, with EPSKQ+ being 9× and 3× faster than the state-ofthe-art schemes in the index build and query phase, respectively

Vertical Federated Learning (VFL) allows collaborative machine learning without sharing local data. However, existing VFL methods face challenges when dealing with heterogeneous local models among participants, which affects optimization convergence and generalization of participants' local knowledge aggregation. To address this challenge, this paper proposes a novel approach calledEmbeddingAggregation-based HeterogeneousModelsTraining in Vertical Federated Learning(EASTER). EASTER focuses on aggregating the local embeddings of each participant's knowledge during forward propagation. We propose an embedding protection method based on lightweight blinding factors, which injects the blinding factors into the local embedding of the passive party. However, the passive party does not own the sample labels, so the local model's gradient cannot be calculated locally. To overcome this limitation, we propose a new method in which the active party assists the passive party in computing its local heterogeneous model gradients. Theoretical analysis and extensive experiments demonstrate that EASTER can simultaneously train multiple heterogeneous models and outperform some recent methods in model performance. For example, compared with the state-of-the-art method, the model accuracy of EASTER was improved by 7.22% under the CIFAR-10 dataset.

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs.

Physical-layer secret key generation (PKG) is an emerging technique for secret key sharing. However, researches on it rarely consider the issue of energy efficiency, which results in a limited performance gain at the expense of a large amount of consumed energy. In this paper, we define the secret key energy efficiency (KEE) as the ratio of the generated secret key bits to the total energy consumption in the resource constrained PKG system. An optimization problem with quality of service (QoS) requirement and power consumption constraints is formulated and a multi-layer iterative algorithm to maximize the KEE is proposed. To cope with the difficulty of the non-convex problem, we transform and iterate it until it is equivalent to the primal problem by applying the Dinkelbach algorithm. In each iteration, the penalty algorithm and difference-of-convex-functions (DC) programming algorithm are used to tackle the non-convex constraints and objectives, respectively. Simulation results demonstrate that the KEE which is maximized can be 84% higher than that of the secret key rate (SKR) maximization only at the cost of a 5% decrease in SKR.

Decentralized identity (DID) is pivotal to Web3 applications as it empowers users to manage their identities and credentials without relying on any central authority. Multi-controller is a new and indispensable scenario outlined by the W3C DID standards, while its privacy and security issues have not yet been fully explored. In this paper, we find two new attacks caused by multiple controllers toward DID management, and propose a privacy-preserving and secure identity management scheme to defend against both attacks. The first proposed controller-correlation attack allows an attacker to infer relationships between different subjects by correlating the public keys uploaded by multiple controllers to the blockchain. To avoid this kind of privacy leakage, we propose a masking scheme based on the Merkle tree, which allows the controllers to prove their ownership over the multi-controller identities without publicizing the plaintext of their public keys. The other identity impersonation attack exploits insecure controller revocation caused by high block synchronization latency. To resist this attack, we propose a lightweight authentication scheme. The holders provide digest freshness proof while the verifiers only need to download block headers. To evaluate the feasibility of our proposed scheme, we implement our system on the Sepolia TestNet. The experimental result demonstrates that our system can prevent these attacks with acceptable gas consumption and time consumption, compared with the state-of-the-art.

The use of log files in digital forensics highlights the importance of ensuring their data integrity for auditing purposes. However, traditional centralized audit log systems face challenges in maintaining data integrity due to log injection attacks and single-point failures. Although blockchain technology can accurately process and replicate log files, existing blockchain-based audit log systems still suffer from security and reliability issues due to their weak threat models and limited scalability. To address these concerns, we propose a blockchain-based audit log system that ensures data integrity under a general threat model where a part of the nodes, including loggers and auditors, are untrusted. First, our proposed system resists collusion attacks by incorporating multiple nodes for system processes and utilizing smart contracts to enforce consensus algorithms. Second, to save blockchain storage space, we design an efficient log integrity proof method, which generates a sub-Non-Fungible Token (sub-NFT) for each log file and keeps it on the blockchain as integrity proof. The single-point failure problem is resolved by outsourcing log files to a distributed file system. To evaluate the proposed system, we implement a prototype based on Hyperledger Fabric. Experimental results show that our proof generation method can reduce storage space usage in comparison to other blockchain-based audit log systems, saving approximately 50% of space in Hyperledger Fabric. The security analysis proves that our system can ensure log file data integrity under the proposed threat model.

Identity management plays a critical role in Web3 applications. Decentralized Identity (DID) offers a privacy-preserving solution, giving users full control over their identity information. Existing research on DID primarily focuses on single-owner scenarios, where owners have complete privileges for owner management and credentials. However, in multi-owner cases, current coarse-grained identity management approaches lead to serious privacy and security problems, such as identity impersonation and high key recovery overhead. Little work has been done on identity management for multiple owners. In this paper, we propose MoDID, a fine-grained identity management scheme for multiple owners, which complies with the DID standard proposed by W3C. First, our solution allows multiple owners to control DID subjects flexibly and reliably through hierarchical owner management. Additionally, we design a secure key recovery scheme to reduce the risk of identity loss while introducing lower overhead. Finally, we implement MoDID on the Sepolia Ethereum Test Network to evaluate the effectiveness of our proposed scheme. The result demonstrates that our system allows multiple owners to manage a single identity with lower gas consumption and time consumption than the state-of-the-art.

The development of the Industrial Internet of Things (IIoT) is impeded by the lack of unknown protocol specifications. Protocol Reverse Engineering (PRE) plays a crucial role in inferring unpublished protocol specifications by analyzing traffic messages. Since different types within a protocol often have distinct formats, inferring the protocol type is essential for subsequent reverse analysis. Natural Language Processing (NLP) models have demonstrated remarkable capabilities in various sequence tasks, and traffic messages of unknown protocols can be analyzed as sequences. In this paper, we propose a framework for clustering unknown industrial control protocol types. Our framework utilizes a transformer-based auto-encoder network to train corresponding request and response messages, leveraging intermediate layer embedding vectors learned by the network for clustering. The clustering results are employed to extract candidate keywords and establish empirical rules. Subsequently, rule-based re-clustering is performed, and its effectiveness is evaluated based on previous clustering results. Through this re-clustering process, we identify the most effective combination of keywords that define the type. We evaluate the proposed framework using three general protocols that have different type rules and successfully separate the protocol internal types completely.

Web 3.0 has been proposed as a new generation of the Internet, which shifts towards system decentralization, improved data security, and self-sovereign identity. With the proliferation of networked entities, the proper management and verification of their identities play a vital role in Web 3.0. Decentralized identity is a promising paradigm to enhance data security and restore sovereignty over personal data to users. However, the data security in existing centralized solutions is often severely limited. In this paper, we propose vDID, a novel blockchain-enabled verifiable decentralized identity management system for Web 3.0. First, we design a generic verifiable DID structure, which is capable of capturing and expressing the inherent relationships between different entities with high granularity. Second, we develop an identity verification scheme to support efficient integrity verification for identities and their relationships in the decentralized framework. We implement vDID and conduct experiments to evaluate the system performance. Experimental results demonstrate the effectiveness of our proposed system.

Ring signatures have been extensively researched for Cloud-assisted Electronic Medical Records (EMRs) sharing, aiming to address the challenge of “medical information silos” while safeguarding the privacy of patients’ personal information and the security of EMRs. However, most existing EMRs sharing systems that utilize ring signatures are vulnerable to quantum attacks, posing a severe challenge for the e-health scenario. To alleviate this issue, some studies have been conducted on lattice-based ring signatures. Nevertheless, there still exist two challenges. Firstly, current schemes fail to verify if multiple EMRs come from the same signer, undermining e-health reliability. Additionally, adversaries can exploit weaknesses in the network security of signers’ secret keys to forge signatures. In this paper, we propose an efficient lattice-based linkable ring signature (LLRS) for EMRs sharing to ensure patient privacy through anonymity, EMRs security through unforgeability, and checking the linkability for multiple signatures. We then present an enhancement scheme, called FS-LLRS, to additionally offer forward security, ensuring the security of previous ring signatures even if the current key has been compromised. To achieve this, we introduce a binary tree structure to divide time periods and leverage lattice basis algorithms for one-way secret key evolution, allowing users to update the secret keys periodically. Ultimately, we conduct a rigorous security analysis and compare our primitives with prior arts. In computational cost, the best performance of our LLRS and FS-LLRS schemes are just 0.17 and 0.34 times compared to others, respectively. Our LLRS scheme only incurs 0.08 times the communication overhead of others.

One area of current research on adversarial attacks is how to generate plausible adversarial examples when only a small number of datasets are available. Current adversarial attack algorithms used to attack these black-box systems face a number of challenges, such as difficulty in training convergence, ambiguous sample images, substitute models collapse, unsatisfactory attack success rates, high query cost, and low defense capability improvement of target models. As a result, constructing plausible adversarial situations in a few known real-world sample circumstances remains difficult. As a solution to the aforementioned issues, this study introduces MC-Net, a novel multi-stage and multi-class balanced generating method based on a limited number of samples to generate realistic adversarial examples. Firstly, a multi-task learning approach is used to train the GAN by fully utilizing the small samples, ensuring that the size of the generated dataset for each category is balanced. In addition, we design a weight-balancing strategy to ensure faster convergence of each sub-network. Then, in the second stage, the generated samples of different categories are used to train a substitute model, and the distillation method is adopted to learn the output distribution of the target model. Finally, adversarial examples are constructed on the generated samples to complete the attack on the target models. Extensive experiments have proven that MC-Net has the following advantages: 1) The substitute model converges quickly using limited samples and queries; 2) High attack success rates can be obtained with a few queries; and 3) The constructed adversarial examples significantly improve the target model’s defense. Furthermore, we only utilize a few queries for the Microsoft Azure online model to obtain a satisfactory result. Our code can be found at https://github.com/jiaokailun/A-fast.

The Payment Channel Network (PCN) has emerged as an extensively adopted solution to address the scalability issues of Bitcoin by efficient off-chain updates. However, conflicts arise while existing update protocols are pursuing multiple goals of security, privacy, and expressiveness. In this work, we propose a new off-chain update protocol, Secure and Anonymous Multi-Channel Updates (SAMCU), which is developed on the basis of Unspent Transaction Output (UTXO). SAMCU aims at achieving goals of internal anonymity, balance security, and multi-channel updates simultaneously, which has not been done before. To achieve these goals, we exploit the technique of updating graph splitting (UGS) to make participants aware of only the identities of their neighboring sub-graphs, thereby ensuring internal anonymity in multi-channel updates. Then, to avoid security issues arising from equal sub-graphs, we further propose an Enable Payment Transaction Tree (EPTT) to guarantee balance security for each honest protocol participant. Moreover, we optimize the performance of our solution, reducing transaction fees by splitting transactions and the number of communication connections by hierarchical communication. To evaluate the performance of the SAMCU, we implement a prototype involving up to 100 updating payment channels. Experimental results demonstrate that SAMCU outperforms the state-of-the-art, resulting in approximately 70% savings in communication connections and a 66% reduction in on-chain transaction fees when the number of updating payment channels is 100.

Adversarial attack algorithms are useful for testing and improving the robustness of industrial AI models. However, attacking black-box models with limited queries and unknown real labels remains a significant challenge. To overcome this challenge, we propose using contrastive learning to train a generated substitute model called attack contrastive learning network (ACL-Net) to attack black-box models with very few queries and no real labels. ACL-Net achieves end-to-end contrastive learning during training without labels, which differs from previous contrastive learning methods that required separate training for the classification layer with labels. We improve ACL-Net's robustness by using adversarial examples to train it during the attack stage. This approach results in more effective adversarial examples generated by ACL-Net. We conducted extensive experiments to validate the effectiveness of ACL-Net. Compared with the latest algorithms, ACL-Net requires fewer queries to achieve better attack performance, demonstrating its superiority in query-efficient black-box attacks. Overall, our approach presents a promising solution to the challenge of attacking black-box models with limited queries and unknown real labels. Our results show the effectiveness of using contrastive learning to train generated substitute models, and the potential for improving the robustness of industrial AI models through adversarial attacks.

Diffusion models have demonstrated their great ability to generate high-quality images for various tasks. With such a strong performance, diffusion models can potentially pose a severe threat to both humans and deep learning models. However, their abilities as adversaries have not been well explored. Among different adversarial scenarios, the no-box adversarial attack is the most practical one, as it assumes that the attacker has no access to the training dataset or the target model. Existing works still require some data from the training dataset, which may not be feasible in real-world scenarios. In this paper, we investigate the adversarial capabilities of diffusion models by conducting no-box attacks solely using data generated by diffusion models. Specifically, our attack method generates a synthetic dataset using diffusion models to train a substitute model. We then employ a classification diffusion model to fine-tune the substitute model, considering model uncertainty and incorporating noise augmentation. Finally, we sample adversarial examples from the diffusion models using the average approximation over the diffusion substitute model with multiple inferences. Extensive experiments on the ImageNet dataset demonstrate that the proposed attack method achieves state-of-the-art performance in both no-box attack and black-box attack scenarios.

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.

Digital twins are poised to enter our lives with Industry 4.0. The Digital Twin Network (DTN) paradigm is projected to deliver upon the promise of efficient collaboration among digital twins to enable complicated and systematic services across many domains, through depicting an overall picture of a group of physical objects. To achieve timely data processing of digital twins, Mobile Edge Computing (MEC) shifts the computational power towards the network edge, and network slicing is well-suited to bundle heterogeneous physical resources to build logical networks based on edge servers for accommodating DTNs. In light of this, in this paper we investigate DTN slicing-enabled service provisioning in MEC, where each DTN slice consists of one master digital twin and a set of worker digital twins, and each worker digital twin is synchronized through collecting data from a respective object periodically. The master digital twin aggregates the processed data from worker digital twins to model the DTN continuously for user query services, whilst meeting delay requirements of users. We capture the utility gain of a DTN slicing request based on the DTN model quality at its master digital twin that is impacted by the Age of Information (AoI), and we focus on two novel optimization problems: the utility maximization problem for a single DTN slicing request, and the dynamic utility maximization problem for multiple DTN slicing requests. We propose an approximation algorithm for the former, and an online algorithm with a provable competitive ratio for the latter. We also evaluate the performance of the proposed algorithms through simulations. Experimental results demonstrate that the proposed algorithms are promising, outperforming their counterparts by at least 10.2%.

The construction of undetectable adversarial examples with few perturbances remains a difficult problem in adversarial attacks. At present, most solutions use the standard gradient optimization algorithm to build adversarial examples by applying global perturbations to benign samples and then launch attacks on the targets (e.g., face recognition systems). However, when the perturbance size is limited, the performance of these approaches suffers substantially. The content of crucial places in an image, on the other hand, will impact the final prediction; if these areas can be investigated and limited perturbances introduced, an acceptable adversarial example will be constructed. Based on the foregoing research, this article offers a dual attention adversarial network (DAAN) to produce adversarial examples with limited perturbations. DAAN initially searches for effective areas in an input image using the spatial attention network and channel attention network, and then creates space and channel weights. Following that, these weights direct an encoder and a decoder to generate effective perturbation, which is then combined with the input to produce an adversarial example. Finally, the discriminator determines if the created adversarial examples are true or false, and the attacked model is utilized to determine whether the generated samples fit the attack targets. Extensive studies on various datasets show that DAAN not only delivers the best attack performance across all comparison algorithms with few perturbations, but it can also significantly improve the defensiveness of the attacked models.

The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example’ searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks.

Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to attack blackbox DNNs with unknown architectures or parameters, which poses threats to many realworld applications. We find that existing transferable attacks do not distinguish between style and content features during optimization, limiting their attack transferability. To improve attack transferability, we propose a novel attack method called style-less perturbation (StyLess). Specifically, instead of using a vanilla network as the surrogate model, we advocate using stylized networks, which encode different style features by perturbing an adaptive instance normalization. Our method can prevent adversarial examples from using non-robust style features and help generate transferable perturbations. Comprehensive experiments show that our method can significantly improve the transferability of adversarial examples. Furthermore, our approach is generic and can outperform state-of-the-art transferable attacks when combined with other attack techniques.11Our code is available at https://github.com/uhiu/StyLess

Though designed with security in mind, blockchains are vulnerable to various kinds of attacks, especially when the network computational power is low. Selfish mining is one of the most rudimentary and notorious attacks, which maliciously renders blocks found by honest miners orphaned by strategically withholding and revealing the found blocks. In this paper, we analyze the profitability of selfish mining under the checkpoint mechanism—a mechanism that has been adopted as a finality gadget by many blockchains like Ethereum and Bitcoin Cash. We develop a rigorous analysis method and conduct quantitative evaluations in various scenarios to explore the mechanism's suppression effect on selfish mining. The results illustrate that the checkpoint mechanism can restrict the profit of selfish mining and increase the threshold of computational power that makes selfish mining profitable, suggesting that it is a practical defense mechanism against selfish mining.

Blockchain technology can construct a distributed and trusted ledger, which can be used for electronic voting (E-voting) systems to ensure the security of voting data and improve government credibility. However, existing blockchain-based solutions cannot fully fulfill five core requirements in E-voting, i.e., auditability, privacy, authentication, correctness, and unreusability, which make them unpractical in the reality. In this paper, we propose a Double Blockchain-based E-voting (DBE-voting) system, which consists of a private blockchain and a public blockchain. In the proposed system, the voter information is only recorded in the private blockchain for further auditing and the voting results are recorded in both blockchains. The voter's privacy can be protected in the private blockchain while the voting results can be queried in the public blockchain for verifying the correctness of the election process. Moreover, the ballot recorded in both blockchains is signed with a valid linkable ring signature to ensure authentication and unreusability. We propose an on-chain and off-chain hybrid storage mechanism to ensure the consistency and correctness of voting data in two blockchains. Experimental results demonstrate that the throughput of our system can reach 29 transactions per second when the block size is 512 KB. The security analysis shows that the DBE-voting is the first blockchain-based system that can meet all five requirements simultaneously.

Ring Confidential Transaction (RingCT) protocol is an effective cryptographic component for preserving the privacy of cryptocurrencies. However, existing RingCT protocols are instantiated from one-out-of-many proofs with only one secret, leading to low efficiency and weak anonymity when handling transactions with multiple inputs. Additionally, current partial knowledge proofs with multiple secrets are neither secure nor efficient to be applied in a RingCT protocol.In this paper, we propose a novel any-out-of-many proof, a logarithmic-sized zero-knowledge proof scheme for showing the knowledge of arbitrarily many secrets out of a public list. Unlike other partial knowledge proofs that have to reveal the number of secrets [ACF21], our approach proves the knowledge of multiple secrets without leaking the exact number of them. Furthermore, we improve the efficiency of our method with a generic inner-product transformation to adopt the Bulletproofs compression [BBB+18], which reduces the proof size to 2⌈log2(N)⌉+9.Based on our proposed proof scheme, we further construct a compact RingCT protocol for privacy cryptocurrencies, which can provide a logarithmic-sized communication complexity for transactions with multiple inputs. More importantly, as the only known RingCT protocol instantiated from the partial knowledge proofs, our protocol can achieve the highest anonymity level compared with other approaches like Omniring [LRR+19]. For other applications, such as multiple ring signatures, our protocol can also be applied with some modifications. We believe our techniques are also applicable in other privacy-preserving scenarios, such as multiple ring signatures and coin-mixing in the blockchain.

Data immutability, transparency and decentralization of blockchain make it widely used in various fields, such as Internet of things, finance, energy and healthcare. With the advent of the Big Data era, various companies and organizations urgently need data from other parties for data analysis and mining to provide better services. Therefore, data sharing and data exchange have become an enormous industry. Traditional centralized data platforms face many problems, such as privacy leakage, high transaction costs and lack of interoperability. Introducing blockchain into this field can address these problems, while providing decentralized data storage and exchange, access control, identity authentication and copyright protection. Although many impressive blockchain-based schemes for data sharing or data exchange scenarios have been presented in recent years, there is still a lack of review and summary of work in this area. In this paper, we conduct a detailed survey of blockchain-based data sharing and data exchange platforms, discussing the latest technical architectures and research results in this field. In particular, we first survey the current blockchain-based data sharing solutions and provide a detailed analysis of system architecture, access control, interoperability, and security. We then review blockchain-based data exchange systems and data marketplaces, discussing trading process, monetization, copyright protection and other related topics.

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

To reduce the transmission cost of blockchain confidential transactions, we propose SymmeProof, a novel communication efficient non-interactive zero-knowledge range proof protocol without a trusted setup. We design and integrate two new techniques in SymmeProof, namely vector compression and inner-product range proof. The proposed vector compression is able to reduce the communication cost to log(n) for n-size vectors. The proposed inner-product range proof converts a range proof relation into an inner-product form, which can further reduce the range proof size with the vector compression technique. Based on these two techniques, SymmeProof can eventually achieve a log(n)-size range proof. The proposed SymmeProof can be used in many important applications such as blockchain confidential transactions as well as arguments for arithmetic circuits satisfiability. We evaluate the performance of SymmeProof. The results show that SymmeProof substantially outperforms representative methods such as Bulletproofs in the proof size without a trusted setup.

In recent years, industrial control systems have evolved toward stability and efficiency, increasing industrial control systems interconnected with the Internet, which means that industrial control systems are facing more serious cyber threats. Thus, it is critical for enterprises to consider issues related to data privacy and network security. Digital twin enables real-time synchronization and simulation of data from various physical components of industrial control systems. However, anomaly detection of twin data is still challenging because existing methods are usually multi-stage with tedious training and detection steps. Therefore, we propose a method called end-to-end anomaly detection with the aim to accomplish real-time anomaly detection quickly and accurately. In order to seek key features, multidimensional deconvolutional network and attention mechanism are applied to our model. The results of this study indicate that our method performs well on precision and F1 score in comparison to the state-of-art methods.

With the massive amounts of data generated by industrial Internet of Things (IIoT) devices at all moments, federated learning (FL) enables these distributed distrusted devices to collaborate to build machine learning model while maintaining data privacy. However, malicious participants still launch malicious attacks against the security vulnerabilities during model aggregation. This article is the first to propose Sybil-based collusion attacks (SCA) in the IIoT-FL system for the vulnerabilities mentioned above. The malicious participants use label flipping attacks to complete local poisoning training. Meanwhile, they can virtualize multiple Sybil nodes to make the local poisoning models aggregated with the greatest possibility during aggregation. They focus on making the joint model misclassify the selected attack class samples during the testing phase, while other nonattack classes kept the main task accuracy similar to the nonpoisoned state. Exhaustive experimental analysis demonstrates that our SCA has a superior performance on multiple aspects than the state-of-the-art.

Prior research has introduced a new scenario of blockchain-assisted clouds where the data owner outsources original data to cloud servers and stores some metadata on the blockchain. Despite some research on key-value query and range query in this hybrid-storage scenario, other more complicated data types are not yet supported. In this article, we conduct pioneering research on authenticated queries for graph data, which is a popular data type such as the knowledge graph data, on the blockchain-assisted cloud. The primary challenge is how to design an authenticated data structure (ADS) that supports authenticated queries and can be easily maintained by the blockchain. To this end, we propose a novel ADS, named PAGB, based on the RSA accumulator and completeness set. It can also prevent the original data from being revealed to the public through blockchain or irrelevant queries. We further optimize our design to be more efficient in terms of communication and computation. The effectiveness and efficiency of PAGB are verified through theoretical analysis and extensive experiments.

Smart contract technologies can be used to implement almost arbitrary business logic. They can revolutionize many businesses such as payments, insurance, and crowdfunding. The resulting birth of decentralized finance (DeFi) has gained significant momentum. Smart contracts and DeFi are now attractive targets for attacks. An important research question is how to protect deployed smart contracts and DeFi. Smart contracts cannot be modified once deployed, namely vulnerabilities cannot be fixed by patching. In this case, vulnerabilities in deployed contracts and DeFi might cause devastating consequences. In this paper, we put forward SolSaviour, a framework for protecting deployed smart contracts and DeFi. The core of SolSaviour is to build a smart contract protection mechanism based on democratic voting using a distributed trusted execution environment (TEE) cluster. Once a vulnerability in deployed contracts or DeFi is found, SolSaviour can destroy the defective contract and redeploy a patched contract via the distributed TEE cluster. Moreover, SolSaviour can migrate funds and state variables from the destroyed contract to the patched one. Compared with previous work, our approach can protect smart contracts and DeFi in a distributed manner, avoiding reliance on privileged users or trusted third parties. Our experiment results show that SolSaviour can protect smart contracts and complex DeFi protocols with feasible overhead.

Abnormal traffic detection is the core component of the network intrusion detection system. Although semisupervised methods can detect zero-day attack traffic, previous work suffers from high false alarms because the trained model is simply based on normal traffic. In this article, we propose an accurate abnormal traffic detection method using pseudoanomaly, consisting of an efficient feature extraction framework and a novel denoise autoencoder-generative adversarial network (DAE-GAN) model. The feature extraction framework adopts an innovative packet window scheme to extract spatial and temporal features from traffic flows. The DAE-GAN model has multiple DAEs to achieve efficient data augmentation and generate high-quality pseudoanomalies. The pseudoanomalies are obtained by adding noise on normal traffic and enhanced by adversarial learning in DAE-GAN. Our semisupervised detection method, exploiting both normal data and generated pseudoanomalies, achieves a precision of 98.6% on the NSL-KDD dataset and 98.5% on the UNSW-NB15 dataset. Compared with the state-of-the-art, the detection precision and recall under different user behaviors are significantly improved. The evaluation on four attack datasets shows that our method has a high flow-wise precision of over 99% and a high recall of 60.6%.

With the advent of the Big Data era, industry, business and academia have developed various data exchange schemes to make data more economically beneficial. Unfortunately, most of the existing systems provide only one-time data exchanges without the ability to track the provenance and transformations of datasets. In addition, existing systems encrypt the data to protect data privacy, which hinders demanders from verifying the correctness of the data and evaluating its value.To provide data traceability and privacy while ensuring fairness during data exchanges, we design and implement ZKDET, a traceable data exchange scheme based on non-fungible token and zero-knowledge, which is able to (i) track all transformations of data during their lifecycle and record them on the blockchain; (ii) provide zero-knowledge proofs to securely guarantee that all complex transformations and data contents are correct and meet specific requirements; and (iii) warrant exchange fairness and data privacy in public storage platforms. Security analysis and evaluations on ZKDET show that it can support traceable data exchange while preserving data privacy and maintaining high throughput despite large data volumes.

Verifiable Searchable Symmetric Encryption (SSE) enables reliable search over encrypted, privacy-preserving data on untrusted clouds. Most existing SSE designs only focus on keyword-file search. However, a more difficult but useful search, range search over encrypted numerical values remains unsolved. Moreover, the fairness of search in the mutual distrusted scenario without public verification, where data users may maliciously deny the results after the local result verification, is not well addressed yet. In this paper, we take the first step to study the public verification problem atop the blockchain for encrypted numerical search. We design a novel verifiable SSE scheme named Slicer based on a Succinct Order-Revealing Encryption (SORE) scheme to achieve range search on numerical data. Our search results are verifiable, updated and privacy-preserving by SSE and maintaining the forward security. We illustrate the security and practicality of our design through rigorous analysis and extensive evaluations respectively.

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

Ethereum, the largest blockchain for running smart contracts, has been widely used, especially in financial and cryptocurrency exchange applications. Among them, Tornado Cash is a typical financial application that protects the privacy of users with anonymous transactions. However, users need to pay prohibitively high gas (transaction fees for smart contract calls) for anonymous transactions, which hinders Tornado Cash from wide applications. To address this issue, we introduced a new approach that shifts the high gas-consuming operations on smart contracts to local users. Furthermore, we use zero-knowledge proofs to ensure the operations are properly executed. The smart contract only needs to verify and update the results, which significantly reduces the gas fees of Tornado Cash. To validate our approach, we implemented a prototype and showed that our proposed method could save more than 61% of gas consumption of current operations while maintaining the privacy feature of Tornado Cash. Finally, we discussed further applications and open problems of our approach.

Mobile edge computing (MEC) is an emerging computational paradigm for providing storage and computing capabilities in network edge, to improve the experience of users, to shorten the delay, and to reduce the energy consumption of mobile devices. In this article, we consider a multiuser and multiserver scenario, where each user has an application composed of multiple independent tasks that need to be executed, and each MEC server is equipped on a base station (BS) for assisting mobile users to execute computation-intensive and time-sensitive tasks. Multiobjective optimization for joint task offloading, power assignment, and resource allocation is studied to maximize the offloading gains of users. A multivariable and multiobjective optimization problem with three objectives is constructed. An efficient multiobjective evolutionary algorithm is developed to solve the problems of minimizing the response time, minimizing the energy consumption, and minimizing the cost. Simulation results verify the effectiveness of our algorithm, and show the method significantly improves the user’s offloading benefits. According to the author’s knowledge, this is the first paper on the exploration of multiobjective optimization of multiuser with multiple tasks and multiserver MEC system, in which the worst user offloading revenue is regarded as the optimization objectives.

Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this article, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDN-enabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-to-destination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage and propose a recent block storage allocation scheme for quick retrieval of missing blocks. We develop data migration algorithms to dynamically reallocate data and block storage to adapt topology changes in the network. The proposed blockchain system can also reach consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 18.4 percent less time and consumes 87 percent less battery power when compared with traditional blockchain systems.

RFID technology has been widely applied in a range of applications such as inventory control, warehouse management and supply chain logistics. Many practical applications need to search a given set of tags (calledwanted tags) to determine which of them are present in the system, which is usually calledtag searching. Existing tag searching protocols suffer performance bottleneck and the time efficiency and need to be improved. The bottleneck stems from two factors. First, the existing methods validate the wanted tags in a random way due to the randomness of the hash function, which unavoidably generate many useless slots. Second, in order to achieve the predefined reliability requirement, the existing methods have to repeatedly validate target tags multiple times. In this paper, we design new tag searching techniques of compact exclusive validation that break through the existing performance bottleneck from two aspects. First, our protocols avoid slot waste. Different from random tag validation, our protocols validate tags orderly by mapping the wanted tags and the slots in a one-to-one manner, which makes the reader be able to validate tags in every slot. Second, our protocols avoid repeated tag responding. By combining two lightweight indicators, we rapidly filter out non-wanted tags so that there is no interference when validating the wanted tags. Hence, we need to validate each wanted tag only once, which avoids redundant validation and greatly improves time efficiency. Our protocols work with the assumption that the rough number of tags in the system can be obtained by using existing estimation algorithms, but they do not need to know exactly which tags are in the system. Theoretical analysis illustrates our methods achieve linear time complexity. Extensive experimental results show that, our best protocol can improve the time efficiency by up to 81 percent when compared with the-state-of-art solution.

Many efforts have been devoted to the development of efficient Network Intrusion Detection System (NIDS) using machine learning approaches in Software-defined Network (SDN). Unfortunately, existing solutions failed to detect real-time and zero-day attacks due to their limited throughput and prior knowledge-based detection. To this end, we propose Griffin, a NIDS that uses unsupervised machine learning expertise to detect both known and zero-day intrusion attacks in real-time with high accuracy. Specifically, Griffin uses an efficient feature extraction framework to capture the sequential features of the traffic packets. Then, it utilizes cluster analysis to reduce the feature scale to achieve low throughput. Moreover, an ensemble autoencoder is built automatically to further extract features with low complexity and high precision to train the model. We evaluate the accuracy, robustness, and complexity of the system using open datasets. The result shows that Griffin’s complexity is about 40% lower, and its accuracy is at most 19% higher than existing NIDS.Additionally, even in the situation with evasion, the Griffin has at most 9% decrease of AUC, which is a good performance compared with other solutions. Furthermore, this paper also utilizes the differential privacy framework during training autoencoders to protect datasets’ privacy which is inherent in machine learning approaches.

Deep learning models are widely used in daily life, which bring great convenience to our lives, but they are vulnerable to attacks. How to build an attack system with strong generalization ability to test the robustness of deep learning systems is a hot issue in current research, among which the research on black-box attacks is extremely challenging. Most current research on black-box attacks assumes that the input dataset is known. However, in fact, it is difficult for us to obtain detailed information for those datasets. In order to solve the above challenges, we propose a multi-sample generation model for black-box model attacks, called MsGM. MsGM is mainly composed of three parts: multi-sample generation, substitute model training, and adversarial sample generation and attack. Firstly, we design a multi-task generation model to learn the distribution of the original dataset. The model first converts an arbitrary signal of a certain distribution into the shared features of the original dataset through deconvolution operations, and then according to different input conditions, multiple identical sub-networks generate the corresponding targeted samples. Secondly, the generated sample features achieve different outputs through querying the black-box model and training the substitute model, which are used to construct different loss functions to optimize and update the generator and substitute model. Finally, some common white-box attack methods are used to attack the substitute model to generate corresponding adversarial samples, which are utilized to attack the black-box model. We conducted a large number of experiments on the MNIST and CIFAR-10 datasets. The experimental results show that under the same settings and attack algorithms, MsGM achieves better performance than the based models.

The security of HTTPS fundamentally relies on SSL/TLS certificates issued by Certificate Authorities (CAs), which, however, are vulnerable to be compromised to issue unauthorized certificates (i.e., certificates issued without domains’ permission). Current countermeasures such as Certificate Transparency (CT) can only detect unauthorized certificates rather than preventing them. In this article, we presentPistis, a framework for issuing authorized and trusted certificates with the distributed ledger and Trusted Execution Environment (TEE) technology. InPistis, TEE nodes validate whether the domain in a requested certificate passes the domain ownership validation (i.e., under corresponding applicants’ control) and submit attested results to a smart contract in the distributed ledger. The smart contract issues a certificate to the applicant when an attested result shows a pass. Therefore,Pistiscan ensure its issued certificates are authorized due to the domain ownership validation mechanism in the TEE. Furthermore, as the issued certificates are stored in a Merkle Patricia Tree (MPT) inPistis, they are trusted and can be verified by a normal user easily. The security ofPistisis formally proved in the Universally Composable (UC) framework. Compared with state-of-the-art,Pistisavoids potential damages by preventing unauthorized certificates from issuing.

Despite increasingly emerging applications, a primary concern for blockchain to be fully practical is the inefficiency of data query. Direct queries on the blockchain take much time by searching every block, while indirect queries on a blockchain database greatly degrade the authenticity of query results. To conquer the authenticity problem, we propose a Verifiable Query Layer (VQL) that can be deployed in the cloud to provide both efficient and verifiable data query services for blockchain systems. The middleware layer extracts data from the underlying blockchain system and efficiently reorganizes them in databases. To prevent falsified data from being stored in the middleware, a cryptographic fingerprint is calculated based on each constructed database. The database fingerprint will be first verified by miners and then written into the blockchain. Moreover, public users can verify the entire databases or several databases that interest them in the middleware layer. We implement VQL together with the verification schemes and conduct extensive experiments based on a practical blockchain system. The evaluation results demonstrate that VQL can efficiently support various data query services and guarantee the authenticity of query results for blockchain systems.

A smart contract cannot be modified once deployed. Bugs in deployed smart contracts may cause devastating consequences. For example, the infamous reentrancy bug in the DAO contract allows attackers to arbitrarily withdraw ethers, which caused millions of dollars loss. Currently, the main countermeasure against contract bugs is to thoroughly detect and verify contracts before deployment, which, however, cannot defend against unknown bugs. These detection methods also suffer from possible false negative results.

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

Unauthorized voice recording via smartphones can leak the talking content stealthily. This would be a serious security threat to those individuals, enterprises and the government who need to keep the conversation confidential. Furthermore, due to the size miniaturization of smartphones, it is hard to find the covert recording from malicious attendees. Existing solutions usually jam the recording with audible noise or electromagnetic emitting. However, the audible noise will seriously interfere with conversation and the effect of electromagnetic emitting will be limited by the distance. In this paper, we propose UltraArray, a pioneering silent ultrasonic anti-recording jammer, which can covertly block recording for a long distance. The principle of covert blocking is inspired by acoustic parametric array theory, which suggests that the audible frequency wave can be spread through the air silently while it is modulated to an inaudible ultrasonic frequency. The modulation used in this paper is double sideband (DSB) modulation. The microphone on the phone will record the audible frequency and filtering out the ultrasonic frequency. The jammer we developed uses an acoustics array to form a beam to spread the signal further. The evaluation shows that the device has a good jamming effect on more than 5 meters for most Android smartphones. It will also work well with more than 2.5 meters effective distance on iPhone XR, which has the active noise control (ANC) function. Those results achieve ten times the interference ability of existing solutions.

Nowadays, blockchain as emerging computer technology is gradually applied to the Internet of Things (IoT) industry. However, high traffic load and network congestion have become the main obstacles to the integration of IoT and blockchain, thanks to the vast amount of IoT devices as well as the considerable information redundancy of blockchain. In IoT-blockchain, the number of IoT devices and the blockchain network scale are supposed to establish an appropriate balance. Therefore, building a precise traffic model between the blockchain and IoT devices is crucial for designing and optimizing an IoT-Blockchain network. This paper proposes an innovative traffic model of the IoT-blockchain system, which describes traffic of blockchain nodes under the different sizes of IoT input and network scale. We implement a lightweight Bitcoin-like blockchain based on PoW and connect with several IoT simulators to conduct various network traffic experiments. Our mathematical model and experimental results demonstrate the relationship between self-similarity, network traffic, blockchain scales, and the number of IoT inputs.

The current research on adversarial attacks aims at a single model while the research on attacking multiple models simultaneously is still challenging. In this paper, we propose a novel black-box attack method, referred to as MBbA, which can attack multiple black-boxes at the same time. By encoding input image and its target category into an associated space, each decoder seeks the appropriate attack areas from the image through the designed loss functions, and then generates effective adversarial examples. This process realizes end-to-end adversarial example generation without involving substitute models for the black-box scenario. On the other hand, adopting the adversarial examples generated by MBbA for adversarial training, the robustness of the attacked models are greatly improved. More importantly, those adversarial examples can achieve satisfactory attack performance, even if these black-box models are trained with the adversarial examples generated by other black-box attack methods, which show good transferability. Finally, extensive experiments show that compared with other state-of-the-art methods: (1) MBbA takes the least time to obtain the most effective attack effects in multi-black-box attack scenario. Furthermore, MBbA achieves the highest attack success rates in a single black-box attack scenario; (2) the adversarial examples generated by MBbA can effectively improve the robustness of the attacked models and exhibit good transferability.

Modular exponentiation, an operation widely utilized in cryptographic protocols to transfer text and other forms of data, can also be applied to Internet-of-Things (IoT) devices with high security requirements. However, due to the high resource consumption of modular exponentiation, IoT devices can face the problem of resource insufficient. Fortunately, the secure outsourcing scheme offers a new solution for resource-constrained devices. In this article, we apply a parallel secure outsourcing scheme to provide the possibility for modular exponentiation operation, which is used in the IoT devices. After that, the task of modular exponentiation is decomposed and we introduce the scheme in more detail. In addition, based on this scheme, we designed an extension scheme for RSA, providing enhanced security for IoT devices. Finally, the analysis of experimental results based on 512-4096 b of data indicates the superiority in scalability and time consumption over the previous schemes.

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in networked environments, such as places of mass gatherings, smart cities and smart nations. For example, UAVs can be deployed to detect violations of lockdown, stay-at-home or social / physical distancing directives during pandemics (e.g. COVID-19). There are, however, security and privacy considerations in such deployments. To achieve secure and efficient authentication of UAVs, solutions such as those based on Point-to-Point (P2P) or a Point- to-Multipoint (P2M) communications have been proposed in the literature. In this article, we present a novel blockchain-based technique to support multi-party authentication to facilitate trustworthy group communications. Specifically, this allows us to provide secure P2P wireless communications and trusted group communication management for UAV networks, while ensuring service efficiency. Evaluation findings from both real-world implementation and simulations demonstrate the utility of the proposed approach.

Mobile cloud computing (MCC) as an emerging computing paradigm enables mobile devices to offload their computation tasks to nearby resource-rich cloudlets so as to augment computation capability and reduce energy consumption of mobile devices. However, due to the mobility of mobile devices and the admission of cloudlets, the connection between mobile devices and cloudlets may be unstable, which will affect offloading decision, even cause offloading failure. To address such an issue, in this paper, we propose a robust computation offloading strategy with failure recovery (RoFFR) in an intermittently connected cloudlet system aiming to reduce energy consumption and shorten application completion time. We first provide an optimal cloudlet selection policy when multiple cloudlets are available near mobile devices. Furthermore, we formulate the RoFFR problem as two optimization problems, i.e., local execution cost minimization problem and offloading execution cost minimization problem while satisfying the task-dependency requirement and application completion deadline constraint. By solving both optimization problems, we present a distributed RoFFR algorithm for CPU clock frequency configuration in local execution and transmission power allocation and data rate control in cloudlet execution. Experimental results in a real testbed show that our distributed RoFFR algorithm outperforms several baseline policies and existing offloading schemes in terms of application completion cost and offloading data rate.

By integrating the micro-sensor on RFID tags to obtain the environment information, the sensor-augmented RFID system greatly supports the applications that are sensitive to environment. To quickly collect the information from all tags, many researchers dedicate on well arranging tag replying orders to avoid the signal collisions. Compared to from all tags, collecting information from a part of tags (i.e., target tags) is more challenging because the collecting process is interfered by useless replying from non-target tags. The existing works of target tag information collection are designed for single reader systems. However, they cannot work efficiently in more common multi-reader scenarios, where each reader lacks knowledge of tag distribution among all readers. In this paper, we propose time-efficient protocols to collect target tag information in multi-reader systems. The high efficiency of our protocol is enabled by two novel designs. First, we develop a technique that quickly detects and silences non-target tags without a priori knowledge of which tags are in the readers' interrogation regions. Second, we design an allocation vector to efficiently arrange the replying order of only target tags. Different from previous bit-vector based approaches that make use of only singleton slots, our allocation vector approach also makes use of collision slots to speed up target tag information collection. We further propose an enhancement protocol which can reconcile the collision slots with higher probability and therefore collect information from more target tags simultaneously. The extensive simulation results demonstrate that our protocols significantly outperform the state-of-the-art protocols in terms of time-efficiency.

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos' spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of 33% and 18.7% on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

The Network Intrusion Detection Systems (NIDS) with machine learning in SDN become increasingly popular solutions. NIDS uses abnormal traffic detection to identify unknown network attacks. Most of today's abnormal traffic detection systems are supposed to continuously update the recognition model in time based on the features from newly collected packets to accurately identify unknown network attack behaviors. However, those existing solutions always require a large number of packets to train the recognition model offline. That means it is impossible to accurately detect the emergence of new cyber-attacks immediately. This paper proposes Griffin, a per-packet anomaly detection system that can dynamically update the training model based on neural networks. The Griffin is executed in SDN environment, utilizing a novel ensemble of autoencoders to collectively filter out abnormal traffic from normal traffic. Meanwhile, the autoencoders are updated based on the root mean square error to adjust the training model. The adjustment is done in an unsupervised manner, which needs no expert to label the network traffic or update the model from time to time. Our evaluations, with the open Datasets provided by Yisroel Mirsky, show that Griffin's time delay is around 0. 1s and its accuracy is 98%. Moreover, we also compare Griffin with other four similar NIDSs and find that Griffin performs the best in terms of Matthews Correlation Coefficient and complexity.

Physical-layer key generation (PKG) in multi-user massive MIMO networks faces great challenges due to the large length of pilots and the high dimension of channel matrix. To tackle these problems, we propose a novel massive MIMO key generation scheme with pilot reuse based on the beam domain channel model and derive close-form expression of secret key rate. Specifically, we present two algorithms, i.e., beam-domain based channel probing (BCP) algorithm and interference neutralization based multi-user beam allocation (IMBA) algorithm for the purpose of channel dimension reduction and multi-user pilot reuse, respectively. Numerical results verify that the proposed PKG scheme can achieve the secret key rate that approximates the perfect case, and significantly reduce the dimension of the channel estimation and pilot overhead.

Advertising income depends on the amount of clicks by users of websites and mobile applications. However, the emergence of click fraud greatly reduces the real benefits of the advertisement. Most existing researches focus on detecting click fraud by analyzing properties and patterns of click data streams, but attackers can construct data that looks legitimate by replaying former data streams. In this paper, we propose a novel system called ClickGuard to detect click fraud attacks. ClickGuard takes advantage of motion sensor signals from mobile devices, since the pattern of motion signals is completely different under real click events and fraud events. To prevent attackers from bypassing the system by faking the time-domain statistical characteristics of original signals, we introduce the MFCC algorithm in feature extraction phase. MFCC algorithm can extract frequency-domain features of original signals in specific frequency bands which are hardly constructed out of thin air. Classifiers are finally constructed using these features and several machine learning algorithms. Experiments show that ClickGuard can achieve the accuracy of 96.71% in general environment and 84.16% when attackers modify the time-domain statistical characteristics of raw data.

In order to achieve the desired performance of crowdsensing, the incentive mechanism, which can stimulate the workers to serve the sensing tasks efficiently, is usually indispensable. Different from the existing research efforts of incentive mechanisms, we propose an incentive mechanism to facilitate the delegation of tasks among the workers in hierarchical crowdsensing. Considering the task converging at some skillful workers, which will degrade the system stability and unbalance the workload among the workers, we construct a Stable and Truthful Incentive Mechanism (STIM) to model and restrict the interactions between the requester and the workers. STIM mechanism comprises a queue control algorithm for the workers and an auction scheme with Multi-sEllers for the Divisible tAsks (MEDA), which exploits an optimal winning bids determination strategy and conducts a truthful payment algorithm. The soundness of the modeling and the accuracy of the analysis are verified through extensive simulations.

IoT-blockchain applications have advantages of managing massive IoT devices, achieving advanced data security, and data credibility. However, there are still some challenges when deploying IoT applications on blockchain systems due to limited storage, power, and computing capability of IoT devices. Applying current consensus protocols to IoT applications may be vulnerable to Sybil node attacks or suffer from high-computational cost and low scalability. In this paper, we propose G-PBFT (Geographic-PBFT), a new location-based and scalable consensus protocol designed for IoT-blockchain applications. The principle of G-PBFT is based on the fact that most IoT-blockchain applications rely on fixed IoT devices for data collection and processing. Fixed IoT devices have more computational power than other mobile IoT devices, e.g., mobile phones and sensors, and are less likely to become malicious nodes. G-PBFT exploits geographic information of fixed IoT devices to reach consensus, thus avoiding Sybil attacks. In G-PBFT, we select those fixed, loyal, and capable nodes as endorsers, reducing the overhead for validating and recording transactions. As a result, G-PBFT achieves high consensus efficiency and low traffic intensity. Moreover, G-PBFT uses a new era switch mechanism to handle the dynamics of the IoT network. To evaluate our protocol, we conduct extensive experiments to compare the performance of G-PBFT against existing consensus protocol with over 200 participating nodes in a blockchain system. Experimental results demonstrate that G-PBFT significantly reduces consensus time, network overhead, and is scalable for IoT applications.

Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applications that require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with commercial RFID devices show that RLLL achieves an ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm from adjacent tags, in which case the state-of-the-art solutions only achieve ordering accuracy of lower than 0.8 with localization latency larger than 3 seconds.

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the Geographical correlation-based RF-data Collection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only 1/28~1/3 of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.

Tag inventory is one of the most fundamental tasks for RFID systems. However, the Framed Slotted Aloha (FSA) protocol specified in the C1G2 standard is of low time-efficiency, because it needs to collect all tags in the system. To improve time-efficiency, research communities proposed a batch of sampling-based approaches, in which the reader only needs to collect a small set of sampled tags instead of all. Although time-efficiency has been improved, existing sampling-based approaches still have two common limitations. First, all tags in the system are assumed to have the same sampling probability. It is unfair that tags attached to differential items (e.g., different values) have the same chance to be sampled and collected. Second, all existing sampling-based approaches stay in theory level and cannot be deployed on Commercial Off-The-Shelf (COTS) RFID devices, because the C1G2 standard does not support the sampling function at all. To deal with the above two limitations, this paper studies the new problem of differential tag sampling-letting each RFID tag be identified with a given sampling probability. In this paper, we use the COTS RFID devices including Impinj Speedway R420 reader and Monza 4QT tags to implement the Differential Tag Sampling (DTS) operation. Then, we apply probabilistic analytics on the collected tag data to address some practically important problems such as Multi-category Tag Cardinality Estimation (MTCE), and Value-based Missing Tag Detection (VMTD). Although the analytics results are not 100 percent accurate, the deviation in the results can be controlled below a small threshold and DTS can significantly improve the time-efficiency. DTS can be easily deployed on the COTS RFID systems, because it is totally compliant with the C1G2 standard. Extensive experiments demonstrate that DTS is able to let each tag take the given sampling probability to be sampled and identified. Moreover, the proposed DTS protocol can significantly reduce the execution time of MTCE and VMTD by nearly 70 percent than the FSA protocol.

The introduction of software-defined networking (SDN) has emerged as a new network paradigm for network innovations. By decoupling the control plane from the data plane in traditional networks, SDN provides high programmability to control and manage networks. However, the communication between the two planes can be a bottleneck of the whole network. SDN-aimed DoS attacks can cause long packet delay and high packet loss rate by using massive table-miss packets to jam links between the two planes. To detect and mitigate SDN-aimed DoS attacks, this paper presents FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks. FloodDefender stands between the controller platform and other controller apps, and conforms to the OpenFlow policy without additional devices. The detection module in FloodDefender utilizes new frequency features to precisely identify SDN-aimed DoS attacks. The mitigation module uses three new techniques to efficiently mitigate attack traffic: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to filter out attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. Our evaluation on a prototype implementation of FloodDefender shows that the defense framework can precisely identify and efficiently mitigate the SDN-aimed DoS attacks with very little overhead.

Crowd density estimation is one of the critical issues in social activities. The traditional solution to this problem is to leverage video surveillance to monitor a crowd. However, this is not accurate for crowd density estimation because it is still hard to identify people from background. In the past few years, more and more people use Wi-Fi enabled smartphones. Smartphones can send Wi-Fi request packets periodically, even when they are not connected to access points. This gives another promising solution to the crowd density estimation even for the public environment. In this paper, we first develop a Wi-Fi monitor detection that can capture smartphone passive Wi-Fi signal information including MAC address and received signal strength indicator. Then, we propose a positioning algorithm based on smartphone passive Wi-Fi probe and a dynamic fingerprint management strategy. In real-world public social activities, a person may have zero, one, two, or multiple smartphones with variant Wi-Fi signals. Therefore, we design a method of computing the probability of a user generating one Wi-Fi signal to identify people population. Finally, we propose a crowd density estimation solution based on Wi-Fi probe packets positioning algorithm. Experiments were conducted in an indoor laboratory class and three public social activities, clearly demonstrated that the proposed solution can effectively and accurately estimate crowd density.

Mining attacks allow attackers to gain an unfair share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among the most well-known are block withholding (BWH), fork after withholding (FAW), and selfish mining. In this paper, we propose two new strategies: power adjusting and bribery racing, and introduce two novel mining attacks, Power Adjusting Withholding (PAW) and Bribery Selfish Mining (BSM) adopting the new strategies. Both attacks can increase the reward of attackers. Furthermore, we show PAW can avoid the "miner's dilemma" in BWH attacks. BSM introduces a new "venal miner's dilemma", which results in all targets (bribes) willing to help the attacker but getting less reward finally. Quantitative analyses and simulations are conducted to verify the effectiveness of our attacks. We propose some countermeasures to mitigate the new attacks, but a practical and efficient solution remains to be an open problem.

Our modern devices are filled with files, directories upon directories. Applications generate huge I/O activities in mobile devices. Directory cache is adopted to accelerate file lookup operations in the virtual file system. However, the original directory cache recursively walks all the components of a path for each lookup, leading to inefficient lookup performance and lower cache hit ratio. In this paper, we for the first time fully investigate the characteristics of the directory entry lookup in mobile devices. Based on our findings, we further propose a new directory cache scheme, called Dynamic Skipping Cache, which adopts an ASCII-based hash table to simplify the path lookup complexity by skipping the common prefixes of paths. We also design a novel lookup scheme to optimize the directory cache hit ratio. We have implemented and deployed DS-Cache on a Google Nexus 6P smartphone. Experimental results show that we can significantly reduce the latency of invoking system calls by up to 57.4%, and further reduce the completion time of real-world mobile applications by up to 64%.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage in the blockchain, and propose a recent block storage allocation scheme for quick retrieval of missing blocks. The proposed blockchain system can also reach mining consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 15% less time and consumes 64% less battery power when compared with traditional blockchain systems.

Vehicularad hocnetworks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban location-based data. We first extract spatially-related and temporally-related features from various urban location-based data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are fed into a novel sparse auto-encoder (SAE) framework with feature correlation constraint to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments by utilizing various real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to asinformation sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.

RFID tags have been widely deployed to report valuable information about tagged objects or surrounding environment. To collect such information, the key is to avoid the tag-to-tag collision in the open wireless channel. Polling, as a widely used anti-collision protocol, provides a request-response way to interrogate tags. The basic polling however needs to broadcast the tedious tag ID (96 bits) to query a tag, which is time-consuming. For example, collecting only 1-bit information (e.g., battery status) but with 96-bit overhead is a great limitation. This paper studies how to design efficient polling protocols to collect tag information quickly. The basic idea is to minimize the length of the polling vector as well as to avoid useless communication. We first propose an efficient Hash polling protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than 16 bits (the number of tags is less than 100,000). We then propose a tree-based polling protocol (TPP) that avoids redundant transmission in HPP. By constructing a binary polling tree, TPP transmits only different postfix of the neighbor polling vectors; the same prefix is reserved without any retransmission. The result is that the length of the polling vector reduces to only 3.4 bits. Finally, we propose an incremental polling protocol (IPP) that updates the polling vector based on the difference in value between the current polling vector and the previous one. By sorting the indices and dynamically updating them, IPP drops the polling vector to 1.6 bits long, 60 times less than 96-bit IDs. Extensive simulation results show that our best protocol IPP outperforms the state-of-the-art information collection protocol.

Network-based malware has posed serious threats to the security of host machines. When malware adopts a private TCP/IP stack for communications, personal and network firewalls may fail to identify the malicious traffic. Current firewall policies do not have a convenient update mechanism, which makes the malicious traffic detection difficult.

Smartphone sensors have been applied to record the movement of users for healthy use. However, the motion sensor readings recorded by malicious applications can be utilized as a side-channel to leak user privacy by keystroke inference. Most existing approaches use time-domain statistical characteristics for keystroke inference. Their systems are poor to show the subtle changes in short time period, since the time- domain statistical features can only reflect the characteristics in a long-time interval. In this paper, we propose a novel framework to perform keystroke inference on smartphones. This framework introduces an improved MFCC algorithm to extract frequency- domain features for more comprehensive use of raw data. Since the frequency-domain energy distribution of motion signals is concentrated, and the specificity of signals is strong, MFCC can improve the inference accuracies under complex scenarios. Based on this framework, we present a prototype called FreqKey, which is an inference system to leak user privacy such as PINs and passwords. FreqKey collects motion sensor readings during keystroke events and constructs classification models with machine learning algorithms. Experimental results show that FreqKey improves the performance in a variety of complex scenarios. Especially, even in web platform whose sampling rate is lower than 80Hz, FreqKey can achieve relatively high accuracy of 74.6%. To mitigate the frequency-based side-channel attack and protect user privacy, we propose a defense solution which contains sensor- activity monitoring, malicious program identification and interference signal injection.

In this paper, we present a new design, named MWS, for application offloading in mobile environments. MWS implements a mobility-aware WiFi selection policy on smartphones with the goal of achieving low delay and energy efficiency. The essence of this work lies in the emphasis of minimizing the number of occurrences of WiFi disconnects by utilizing human mobility habits and cloud-assisted WiFi information profiling. The WiFi access points (AP) selected by MWS are predicted to maintain the longest connection with smartphones among all available APs. As a result, MWS manages to avoid unnecessary or unsuccessful handoff that may otherwise be caused by the default signal strength oriented WiFi selection policy. The benefits brought by MWS are validated in our real-world evaluation. Compared with the default policy, MWS effectively reduces the number of occurrences of WiFi disconnects, and achieves a reduction of up to 50% in energy consumption and up to 66% in data communication time for application offloading in mobile environments.

Named Data Networking (NDN) is a promising technology for content centric networks, and it is suitable for vehicular networks since no IP architecture is required. Quite a number of solutions have been proposed for vehicular NDN (V- NDN), but high communication cost due to frequent topology changes caused by high mobility of vehicles is still a challenge to be addressed. In this paper, we study how to disseminate traffic information to vehicles via V-NDN. Different from existing works, we consider navigation route based data interests, i.e., a vehicle is concerned about the traffic information along road segments planned to take. According to such a data interest scenario, we propose a tree based data interest structure and associated maintenance operations to merge identical data interests due to overlapping navigation routes among different vehicles. With the tree based data interest management, the number of interest packets can be significantly reduced. Then, we propose trigger based mechanisms for data interest packet re-sending and forwarding, which can avoid unnecessary interest packets re-sending. With our design, traffic information can be disseminated to interested nodes with high success ratio and low communication cost simultaneously. Simulations via SUMO and ndnSIM confirm such advantages of our work.

Traditional mobile crowdsensing photo selection process focuses on selecting photos from participants to a server. The server may contain tons of photos for a certain area. A new problem is how to select a set of photos from the server to a smartphone user when the user requests to view an area (e.g., a hot spot). The challenge of the new problem is that the photo set should attain both photo coverage and view quality (e.g., with clear Points of Interest). However, contributions of these geo-tagged photos could be uncertain for a target area due to unavailable information of photo shooting direction and no reference photos. In this paper, we propose a novel and generic server-to-requester photo selection approach. Our approach leverages a utility measure to quantify the contribution of a photo set, where photos' spatial distribution and visual correlation are jointly exploited to evaluate their performance on photo coverage and view quality. Finding the photo set with the maximum utility is proven to be NP-hard. We then propose an approximation algorithm based on a greedy strategy with rigorous theoretical analysis. The effectiveness of our approach is demonstrated with real-world datasets. The results show that the proposal outperforms other approaches with much higher photo coverage and better view quality.

With the development of sensing technology, smartphones can provide various kinds of data, including inertial sensing data, WiFi data, depth data, and images. These data make it possible to construct accurate indoor floor plans that are the critical foundations of flourishing indoor location-based services for smartphone. However, even with the popular crowdsourcing approach, the wide construction of indoor floor plans has not yet to be realized due to the intensive time consumption. In this paper, we utilize deep learning techniques to build PlanSketcher, a system that enables one user to construct fine-grained and facility-labeled indoor floor plans accurately. First, the proposed system extracts novel integrated features to recognize diverse landmarks. Second, traverse-independent hallway topologies are constructed based on the sensing data, depth data, and images through the proposed hallway construction algorithms. Finally, PlanSketcher constructs the room shape and labels recognized facilities in their corresponding positions to generate a complete indoor floor plan. Because PlanSketcher exploits different kinds of data collected from smartphones with new feature extraction method, it can obtain accurate indoor floor plan topology and facility labels. We implement PlanSketcher and conduct extensive experiments in three large indoor settings. The evaluation results show that the 90th percentile accuracy of positions and orientations of facilities are 1 m–2.5 m and 4°–6°, while 85%–95% facilities are recognized and labeled precisely.

Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose CrowdGIS, an automatic store selfupdating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user's shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy.

In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) bytrading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays undertwo scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation, and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power, and power splitting ratio.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

Wireless key generation in slow fading channels is challenging because of the limited channel variation and randomness. This paper proposes a novel artificial randomness (AR) assisted approach for fast key generation in slow fading environments. It integrates user-designed randomness into the channel probing to form a fast-changing combined channel to realize information-theory security. The analytical expressions of secret key capacity are derived. We find that it is possible to improve secret key capacity by introducing AR when legitimate users have a better channel condition than that of eavesdropper. We also find that the improved secret key capacity is proportional to the channel probing number and is bounded by the noise variance and channel condition. Simulation and experimental results show that AR approach can generate secret key effectively in slow fading environments by carefully designing probing numbers. Compared to existing work in literature, the proposed approach does not rely on multiple antennas or extra helpers, and it can be applied in both single antenna and multi-antenna systems.

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban data. We first extract spatially-related and temporally-related features from various urban data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are feeded into a sparse auto-encoder (SAE) model to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments based on real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.

Link State Advertisement (LSA) reflects the current status of all incident links of a router in an Autonomous System (AS). A fake LSA with false link status information will pollute the view of the network topology on routers. In this paper, we present two novel attacks that inject malicious Link State Advertisements (LSAs) to modify the routing tables: adjacency spoofing and single path injection. Adjacency spoofing attack makes attacker access to routing networks by disguising as a legitimate router. Single path injection attack evades the “fight-back” mechanism and affects routing advertisements of routers. Unlike existing LSA injection attacks, which need to be launched by malicious routers, a common host can launch these attacks and control the transmission path of data traffic in an AS. Simulation and real-world experiment results show that these two attacks can efficiently modify the routing tables of routers, and further lead to DNS spoofing, phishing Website, eavesdropping, and manin-the-middle attacks. Furthermore, we also implement a security vulnerability detection system to detect the existing vulnerabilities of routing protocol deployed in real-world routers.

Route planning is an important service in the map navigation. However, most of commercial map applications provide an optimal path that only minimize a single metric such as distance, time or other costs, while ignoring a critical criterion: safety. When citizens or travellers walk in a city, they may prefer to find a safe walking route to avoid the potential crime risk and to have a short distance, which can be formulated as a multi-objective optimization problem. Many previous methods are proposed to solve the multi-objective route planning, however, most of them are not efficient or optimized in a large-scale road network. In this paper, we propose a reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to route planning in a smart city. We conduct experiments on the safety index map constructed based on the historical urban data of the New York city. Comprehensive experimental results show that the proposed approach is almost 34 and 1.4 times faster than the exact multi-objective optimization algorithm and the NSGA-II algorithm respectively. Moreover, it can obtain more than 80% Pareto optimal solutions in a large-scale road network.

In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag's memory as required. Since such information reflects the category attributes, all tags in the same category carry the identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag's response in a category is sufficient. In this paper, we investigate the new problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose an efficient two-phase sampling protocol (TPS). By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category by broadcasting only 7.5-bit polling vector (very efficient when compared to the 96-bit tag ID). We theoretically analyze the protocol performance and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that TPS outperforms the benchmark, greatly improving the sampling performance.

WiFi networks are vulnerable to rogue AP attacks in which an attacker sets up an imposter AP to lure mobile users to connect. The attacker can eavesdrop on the communication, severely threatening users' privacy. Existing rogue AP detection solutions are confined to some specific attack scenarios (e.g., by relaying the traffic to a target AP) or require additional hardware. In this paper, we propose a crowdsensing based approach, named CRAD, to detect rogue APs in camouflage without specialized hardware requirement. CRAD exploits the spatial correlation of RSS to identify a potential imposter, which should be at a different location from the legitimate one. The RSS measurements collected from the crowd facilitate a robust profile and minimize the inaccuracy effect of a single RSS value. As a result, CRAD can filter out abnormal samples sensed in the realtime by dynamically matching the profile. We evaluate our approach with both a public dataset and a real prototype. The results show that CRAD can yield 90% detection accuracy and precision with proper crowd presence, even when the rogue AP is launched close to the legitimate one (e.g., within 1m).

The separated control and data planes in software-defined networking (SDN) with high programmability introduce a more flexible way to manage and control network traffic. However, SDN will experience long packet delay and high packet loss rate when the communication link between two planes is jammed by SDN-aimed DoS attacks with massive table-miss packets. In this paper, we propose FloodDefender, an efficient and protocol-independent defense framework for SDN/OpenFlow networks to mitigate DoS attacks. It stands between the controller platform and other controller apps, and can protect both the data and control plane resources by leveraging three new techniques: table-miss engineering to prevent the communication bandwidth from being exhausted; packet filter to identify attack traffic and save computational resources of the control plane; and flow rule management to eliminate most of useless flow entries in the switch flow table. All designs of FloodDefender conform to the OpenFlow policy, requiring no additional devices. We implement a prototype of FloodDefender and evaluate its performance in both software and hardware environments. Experimental results show that FloodDefender can efficiently mitigate the SDN-aimed DoS attacks, incurring less than 0.5% CPU computation to handle attack traffic, only 18ms packet delay and 5% packet loss rate under attacks.

Energy saving solutions on smartphone devices can greatly extend a smartphone's lasting time. However, today's push services require keep-alive connections to notify users of incoming messages, which cause costly energy consuming and drain a smartphone's battery quickly in cellular communications. Most keep-alive connections force smartphones to frequently send heartbeat packets that create additional energy-consuming radio-tails. No previous work has addressed the high-energy consumption of keep-alive connections in smartphones push services. In this paper, we propose Single Connection Proxy (SCoP) system based on fog computing to merge multiple keep-alive connections into one, and push messages in an energy-saving way. The new design of SCoP can satisfy a predefined message delay constraint and minimize the smartphone energy consumption for both real-time and delay-tolerant apps. SCoP is transparent to both smartphones and push servers, which does not need any changes on today's push service framework. Theoretical analysis shows that, given the Poisson distribution of incoming messages, SCoP can reduce the energy consumption by up to 50%. We implement SCoP system, including both the local proxy on the smartphone and remote proxy on the “Fog”. Experimental results show that the proposed system consumes 30% less energy than the current push service for real-time apps, and 60% less energy for delay-tolerant apps.

Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking products. The grouping problem is to efficiently inform all tags which groups they belong to, such that tags in the same group have the same group ID. With this information, the reader can transmit data through efficient multicast rather than tedious unicast. However in the dynamic RFID system, where the group IDs change frequently, existing works suffer from low time- efficiency. In light of this, this paper proposes the binary grouping (BIG) protocol to improve the grouping performance in the dynamic RFID system. BIG follows three design principles. First, for the to-be-grouped tags, BIG separates them from the entire tag set before grouping, so that the not-to-be-grouped tags will not participate in the following grouping, reducing the running delay. Second, for the update of group ID, BIG flips only the bits of group ID which differ from the previous ones, instead of updating all bits of the group ID, saving the communication overhead. Third, BIG adopts a Huffman coding based grouping scheme, which classifies tags into different groups in an efficient way. The extensive simulations show BIG outperforms the existing promising works.

"Cloud" is a common metaphor for an Internet accessible infrastructure (e.g., data storage and computing hardware) that is hidden from users. Cloud computing makes data truly mobile and a user can simply access a chosen cloud with any internet accessible device. In cloud computing, IT-related capabilities are provided as services, accessible without requiring detailed knowledge of the underlying technology. Thus, many mature technologies are used as components in cloud computing, but still there are many unresolved and open problems. This special issue includes articles addressing the state-of-the-art in strengthening performance and security and cloud computing. Eight representative research articles were carefully selected based on the original presentations at the 2016 International Conference on Cloud Computing and Big Data (CloudCom-Asia'16). The objective of this conference is to bring together researchers who work on cloud computing and related technologies. According to whether its research theme relates more to performance or security, the accepted papers are briefly described in the remaining part of this section. Task scheduling is critical for guaranteeing cloud performance. In the past years, more and more business-to-consumer and enterprise applications start running in the heterogeneous cloud. Such cloud bag-of-tasks (BoT) applications are usually budget-constrained and their scheduling is an essential problem for cloud provider. The problem is even more complex and challenging when the accurate knowledge about task execution time is unknown in advance. Focusing on these challenges, Tang et al1 build a cloud resource management architecture and stochastic task model, which divides cloud task into two execution parts. Then they deduce BoT applications schedule length and total cost according to heterogenous clouds online feedback information. They further formulate this stochastic scheduling problem as a linear programming problem and propose a time and cost multi-objective stochastic task scheduling genetic algorithm, which can find Pareto-optimal schedules for stochastic cloud task that meet its budget constraint. With the rapid development of Internet and cloud computing, the high performance requirements for data center networks (DCNs) are increasing for meeting the need of users. A large number of data need to be processed and shared among servers in a data center. Recently, multicast traffic in DCNs has attracted much attention from academia due to the fact that multicast traffics have the dominating advantages for group communications in DCNs. Therefore, the appropriate multicast traffic scheduling in data center networks cannot only improve network efficiency but also save network resources. Li et al2 propose a multicast scheduling algorithm to appropriately schedule flows to achieve traffic load balance so that network blocking can be avoided. In order to reduce the network blocking, they propose an efficient blocking cost-driven multicast scheduling algorithm in fat-tree DCNs. The paper establishes the blocking model of multicast network based on multicast network state and presents the blocking probability at the next time-slot, which can reduce the scheduling delay of multicast traffic. Furthermore, the paper presents also an optimal selection mechanism of feasible links based on the given link blocking probability at the next time-slot. In order to study cloud performance from a more comprehensive perspective, Wang et al3 investigate how to decide key parameters in service-oriented cloud computing systems to improve the system's performance and maximize the service provider's profit. The paper proposes a multiple game model to formulate the critical parameters decision process. For games among different participators, different rules are used to estimate corresponding key parameters. The proposed MG model can achieve Pareto-optimal equilibrium point, and its efficiency in dynamically deciding key parameters in CCSs are demonstrated by simulation results. Underlying infrastructure plays also a vital role for cloud performance. Newly emerging networking paradigms like Software-Defined Networking (SDN) promises more advances like flexibility and efficiency to cloud management. He et al4 propose NetCore-M language, a high abstraction level programming language for SDN. It can support for packet drop and conflicts detection. NetCore-M language provides a more abstract programming language for network configuration in data center. Specifically, the paper describes in detail the syntax, semanteme, and implementation of NetCore-M language as well as network policy conflict. Besides, this paper verifies that the modified multi-policies combination algorithm can effectively detect policy conflicts based on the implementation of the Pyretic project. Security of applications in multi-cloud collaborative environments is a major concern in today's distributed computing environment. Multi-cloud collaborative environments are highly heterogeneous. The security issues in such environments most commonly arise due to the use of ineffective access control mechanisms. The primary goal of Attribute-based Access Control (ABAC) as an access control model is to fulfill the requirements of highly heterogeneous environments such as multi-cloud environment. There are two major challenges for a system employing ABAC. The first is to determine suitable attributes for users and resources in the system. Formation of the correct set of ABAC rules is another major challenge. John et al5 investigate the development of two alternative approaches for deriving the minimum number of ABAC rules in a multi-cloud environment. In the first approach, they consider forming a minimal set of positive authorizations only. The second approach shows the advantage of developing negative authorizations along with positive authorizations. Together, these two contributions extend the current state-of-the-art in cloud security. With the advent of cloud computing, more and more consumers prefer to use the cloud services with the pay-as-you-consume mode. The cloud storage brings about great convenience to users, who store data in cloud and access to it using the smart devices anytime and anywhere. Consumers' information should be encrypted to guarantee the data privacy. Flexible searching on ciphertext is a critical challenge to be solved for effective data utilization. Yang et al6 propose a novel semantic keyword searchable proxy re-encryption scheme for secure cloud storage. The scheme is quantum attack resistant, while most of the available searchable encryption schemes are not. It not only supports exact keyword search, but also synonym keyword search. Moreover, the data owner is capable to delegate his search right to another user using the proxy re-encryption mechanism. In the generation process of re-encryption key, the delegator and delegate do not need to interactive with each other. The scheme is also collusion resistant. Under the learning with errors hardness problem, this scheme is proved secure in standard model. Wu et al7 investigates how to prohibit massive Twitter spams from cloud. This paper leveraged the massive posts information from social network platforms especially Twitter. Learning from millions of text-based tweets (Twitter messages), algorithms were generated to detect social spammers who propagate suspicious information. The authors developed an innovative spam detection method in Twitter using deep learning techniques, which may contribute to the field in terms of protecting cyber security. They put forward a new Twitter spam detection method based on deep learning to address the problems of existing methods. A series of empirical and theoretical analysis have been adopted to prove the outperformance of the proposed method. These studies will contribute to future analysis and optimization on Twitter spam detection. Meanwhile, more and more client applications for cloud are based on mobile devices. Thus, the security of mobile operating systems is crucial for securing the cloud applications. Qiang et al8 embark on solving the covert channel issues in smartphone operating systems, which may lead to furtive data transmission between applications with different permissions that might threaten users' privacy. The authors propose a general method that can detect covert channel attacks at runtime without impacting the accessibility of shared resources in the system. The method allows users to describe and audit the target covert channels in the application layer as well as the OS layer, by making use of Java hooks and kernel audit tool auditd. The main idea of the method is to track and audit the use of system resources known as potential covert channel variables and impose interferences on those channels to reduce their capacity once violations are detected. They implement a prototype framework to audit and interfere covert communication in both the application layer and the native layer of Android. The experimental results demonstrate that the proposed method can effectively reduce the data rate of user-defined covert channels while the overhead is negligible. The papers presented in this special issue provide research articles related to recent advances in cloud computing. In particular, these research articles aim to strengthen cloud performance and security, from various aspects including task scheduling and underlying SDN infrastructure. We hope that the readers of this special issue will benefit from the research ideas and concepts presented in these research articles. The guest editors of this special issue would like to express their special thanks to all of the authors who submitted their papers to this special issue and to all reviewers who contribute to the paper selection process. We would also like to deeply thank Professor Geoffrey C. Fox, the Editor-in-Chief, for providing the opportunity to publish this special issue and for offering continuous support, encouragement, and guidance throughout this publishing project.

Radio frequency identification (RFID) is an automatic identification technology that brings a revolutionary change to quickly identify tagged objects from the collected tag IDs. Considering the misplaced and newly added tags, fast identifying such unknown tags is of paramount importance, especially in large-scale RFID systems. Existing solutions can either identify all unknown tags with low time-efficiency, or identify most unknown tags quickly by sacrificing the identification accuracy. Unlike existing work, this paper proposes a protocol that utilizes physical layer (PHY) information to identify the intact unknown tag set with high efficiency. We exploit the physical signals in collision slots to separate unknown tags from known tags, a new technique to speed up the ID collection. Such new technique was verified in an RFID prototype system using the USRP-based reader and WISP tags. We also evaluated our protocol to show the efficiency of leveraging PHY signals to successfully get all unknown tag IDs without wasted known tag ID transmission. Simulation results show that our protocols outperform prior unknown tag identification protocols. For example, given 1000 unknown tags and 10 000 known tags, our best protocol has 56.8% less time to the state-of-the-art protocol when collecting all unknown tag IDs.

For large-scale RFID systems, this paper studies the practically important problem of target tag information collection, which aims at collecting information from a specific set of target tags instead of all. However, the existing solutions are of low time-efficiency because of two reasons. First, the serious collisions among tags due to hashing randomness seriously reduce the frame utilization, whose upper bound is just 36.8 percent. Second, they cannot efficiently distinguish the target tags from the non-target tags and thus inevitably collect a lot of irrelevant information on non-target tags, which further deteriorates the effective utilization of the time frame. To overcome the above two drawbacks, this paper proposes the minimal Perfect hashing-based Information Collection (PIC) protocol, which first leverages lightweight indicator vectors to establish a one-to-one mapping between target tags and slots, thereby improving the frame utilization to nearly 100 percent; and then uses the novel data structure called Minimal Perfect Hashing based Filter (MPHF) to filter out the non-target tags, thereby preventing them from interfering with the process of collecting information from target tags. Sufficient theoretical analyses are also presented in this paper to minimize the execution time of the proposed PIC protocol. Extensive simulations are conducted to compare the proposed PIC protocol with prior works side-by-side. The simulation results demonstrate that PIC significantly outperforms the state-of-the-art protocols in terms of time-efficiency.

Radio Frequency Identification (RFID) brings a revolutionary change in a range of applications by automatically monitoring and tracking products. With the proliferation of RFID-enabled applications, multiple readers are needed for ensuring the full coverage of numerous RFID tags. In this paper, we focus on the tag distribution problem in multi-reader RFID systems. The problem is to fast identify the tag set beneath each reader, which is a fundamental premise of efficient product inventory and management. Only with such tag set information can we localize specific tags in a reader and expedite the tag query information collection. As an RFID system usually contains a large number of tags and multiple readers, the traditional solution to identify tags by individual readers is highly time inefficient. We propose an Inference-Based protocol (IB) that identifies the tag distribution based on information inference rules and the aggregated physical signals to improve operational efficiency. In our protocol, three kinds of inference rules based on internal information reported by a single reader, external information shared by multiple readers, and history information retained by the system are fully exploited to infer tag distribution. With these rules, all readers can cooperatively work together and quickly obtain the tag distribution in the system. We also build a prototype RFID system using the USRP-based reader and WISP programmable tags, and then implement the IB protocol. The experimental results and extended simulations show that IB outperforms the state-of-the-art protocols.

With the increasing popularization of radio frequency identification (RFID) technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags (REB), the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency.

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost.

RFID technology has been widely adopted for real-world applications, such as warehouse management, logistic control, and object tracking. This paper focuses on a new angle of applying RFID technology-monitoring the temporal change of a tag set in a certain region, which is called churn estimation. This problem is to provide quick estimations on the number of new tags that have entered a monitored region, and the number of pre-existing tags that have departed from the region, within a predefined time interval. The traditional cardinality estimator for a single tag set cannot be applied here, and the conventional tag identification protocol that collects all tag IDs takes too much time, especially when the churn estimation needs to perform frequently to support real-time monitoring. This paper will take a new solution path, in which a reader periodically scans the tag set in a region to collect their compressed aggregate information in the form of empty/singleton/collision time slots. This protocol can reduce the time cost of attaining pre-set accuracy by at least 35%, when comparing with a previous work that uses only the information of idle/busy slots. Such a dramatic improvement is due to our awareness of collision slot state and the full utilization of slot state changes. Our proposed churn estimator, as shown by the extensive analysis and simulation studies, can be configured to meet any pre-set accuracy requirement with a statistical error bound that can be made arbitrarily small.

An RFID system can greatly improve the efficiency of tagged object inventory setup and update. It is necessary to periodically take stock of tags and update the inventory accordingly (i.e., deleting absent tags and adding new tags) in dynamic scenarios such as warehouses and shopping malls. Fast tag stocktaking is critical for the dynamic RFID system management. Previous work can take stock of tags by either collecting IDs of all the tags in the system, which is known to be inefficient, or broadcasting a long indicator vector to save tag identification time, which is not compatible with current commercial-off-the-shelf (COTS) tags. In this paper, we propose HARN, a protocol that can quickly take stock of tags in dynamic RFID systems but is compatible with COTS RFID tags and easily applied in a real RFID system. HARN uses only one more hash in the standard EPC C1G2 protocol. It leverages the new hash to generate the random number (RN) for a tag that can be used for both channel contention and known tag recognition, which can save the tedious ID transmission from known tags to readers and greatly speed up the stocktaking of tags. Simulation results demonstrate that HARN improves stocktaking throughput by up to 3.8x when compared to the state-of-the-art solutions in dynamic RFID systems.

Energy efficient prefetching systems for smart-phones can greatly reduce energy consumption and data transmission, and maintain the timely response when information is prefetched. However, the proxy structure of the system can cause security problem to reveal private information to the third party. The end-to-end encryption (SSL) in traditional prefetching systems cannot solve the security problem in this new, complex energy efficient prefetching system. In this paper, we propose Secure and Energy Efficient Prefetching (SEEP) to meet the security requirement of HTTPS connections and to save smartphone's energy consumption and data transmission. The new design of SEEP includes two parts: the local proxy on the smartphone to verify the validity of prefetched responses, and the remote proxy (e.g. on the cloudlet) to store encrypted prefetched responses. SEEP is transparent to both smartphones and web servers, which does not need to change today's Browser/Server framework. Security analysis shows that SEEP protects the confidentiality of requests and responses, and is able to resist replay attack from malicious proxy. Experimental results show that the proposed system consumes 25% less energy and 95% less data when prefetching 10 outbound webpages than the traditional prefetching system in Wi-Fi networks.

Fast tag identification is a fundamental challenging problem in multi-reader RFID systems. The challenge is how to effectively handle Reader-Tag (RT) collisions and Reader-Reader (RR) collisions among adjacent readers. These collisions are caused by interfering signals simultaneously transmitted by the readers, and may disallow adjacent readers to work together. Prior works tackle this problem by scheduling adjacent readers to work in different time slots. The readers that are selected to simultaneously work, however, are usually only a small proportion of the total readers. This greatly restricts the identification throughput. Our insightful investigation on the current tag identification protocol reveals that RT collisions are caused by asynchronous actions of readers. i.e., a reader transmits signal while its adjacent readers receive. We thus develop Slot Splitting, a technique that can completely eliminate RT collisions by synchronizing actions of readers. We also propose a reader selection algorithm that minimizes RR collisions by selecting a reader set with maximum reading efficiency. The combination of these new techniques inspires Federal, a Fast and efficient tag identification protocol with interference-elimination-based reader scheduling. To our knowledge, Federal is the first identification protocol that completely eliminates RT collisions and minimizes RR collisions in both regularly and randomly deployed multi-reader systems. We validate the feasibility of Slot Splitting with experiments on the USRP platform and evaluate the performance of Federal through extensive simulations. The results show that Federal can increase tag identification throughput by up to 218% compared with the state-of-the-art work.

Polling is a widely used anti-collision protocol that interrogates RFID tags in a request-response way. In conventional polling, the reader needs to broadcast 96-bit tag IDs to separate each tag from others, leading to long interrogation delay. This paper takes the first step to design fast polling protocols by shortening the polling vector. We first propose an efficient Hash Polling Protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than log(n) bits (n is the number of tags). We then enhance HPP (EHPP) to make it not only more efficient but also more steady with respect to the number of tags. To avoid redundant transmissions in both HPP and EHPP, we finally propose a Tree-based Polling Protocol (TPP) that reserves the invariant portion of the polling vector while updates only the discrepancy by constructing and broadcasting a polling tree. Theoretical analysis shows that the average length of the polling vector in TPP levels off at only 3.44, 28 times less than 96-bit tag IDs. We also apply our protocols to collect tag information and simulation results demonstrate that our best protocol TPP outperforms the state-of-the-art information collection protocol.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Wearable cameras require connecting to cellular-capable devices (e.g., smartphones) so as to provide live broadcast services for worldwide users when Wi-Fi is unavailable. However, the constantly changing cellular network conditions may substantially slow down the upload of recorded videos. In this paper, we consider the scenario where wearable cameras upload live videos to remote distribution servers under cellular networks, aiming at maximizing the quality of uploaded videos while meeting the delay requirements. To attain the goal, we propose a dynamic video coding approach that utilizes dynamic video recording resolution adjustment on wearable cameras and Lyapunov based video preprocessing on smartphones. Our proposed resolution adjustment algorithm adapts to network condition changes, and reduces the overheads of video preprocessing. Due to the property of Lyapunov optimization framework, our proposed video preprocessing algorithm delivers near-optimal video quality while meeting the upload delay requirements. Our evaluation results show that our approach achieves up to 50% reduction in power consumption on smartphones and up to 60% reduction in average delay, at the cost of slightly compromised video quality.

This paper studies the problem of data collection in sensor-augmented RFID networks: how to quickly obtain the error-bounded data from sensor-augmented RFID tags. Existing data collection protocols require each tag to transmit the sensor data to the reader through a low-rate channel. However, in large-scale RFID system, they take too long time and block other time-sensitive operations. By exploring the correlation of sensor data, our Sampling-based Information Collection (SIC) protocol significantly reduces the number of responding tags. Specifically, SIC obtains an error bound based on the estimation model by using some randomly-sampled data. The error bound is expected to maximize the number of data within it. These data can be seen as a cluster and be approximated by one value within the error bound. Then, SIC only needs to collect the data of out this cluster, thereby significantly reducing the data transmission. It minimizes the execution time by optimizing the sample size and estimating the number of tags out of the error bound. We conduct extensive simulations to evaluate the performance of SIC and compare it with three major related work. The results demonstrate that SIC is 1 to 10 times faster than the state-of-the-art solution.

Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking products. For many RFID-enabled applications, fast searching a particular group of products is practically important in a large-scale RFID system. Different from previous searching protocols, we propose a physical layer tag searching (PLAT) protocol, which makes three fundamental improvements. First, PLAT can exactly pinpoint the search result without false positives at a small delay expense. Second, based on the physical layer signals, PLAT can interpret the accurate number of tags replying in a collision slot, speeding up the execution of tag searching. Third, PLAT can take the global view of the accurate replying information from slots to extract each tag identifier, further improving the protocol performance. We also implement a prototype system based on the USRP and WISP platform. Experimental results validate the feasibility of our protocol. The extensive simulations show PLAT produces the performance gain by a factor of above 2 compared with the state-of-the-art works.

Tag scanning is an important issue to dynamically manage tag IDs in radio frequency identification (RFID) systems. Different from tag identification that collects IDs of all the tags, tag scanning first verifies whether or not a responding tag has already been identified and retrieves its ID when the answer is yes, and collects the tag's ID only when it is unidentified. In this paper, we present the first study on spot scanning with a handheld reader, which aims to scan tags in the reader's interrogation range at an arbitrarily specified position in the system. Existing studies mainly focus on continuous scanning, and they are highly time inefficient in performing spot scanning. The inefficiency stems from the small overlap between tag populations in different spot scanning operations, in which case existing solutions cannot efficiently recognize unidentified tags. We develop a novel technique called LOCK to efficiently recognize unidentified tags even when the overlapped tags are few. LOCK does not simply use a tag's reply slot index but also compact short responses from tags to efficiently distinguish unidentified tags from identified ones. The valuable compact short responses are firstly investigated, which are the keys for efficient tag identification in the paper. Based on LOCK, three tag scanning protocols are proposed to solve the spot scanning problem. Simulation results show that, for spot scanning, our best protocol reduces per tag scanning time by up to 70 percent when compared with the state-of-the-art solution. Moreover, the proposed protocols can also be employed to perform continuous scanning with better time efficiency than the best existing solutions.

The grouping problem in RFID systems is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of$m$tags simultaneously in multicast, which improves the efficiency by$m$times when comparing with unicast. This paper studies this practically important but not yet thoroughly investigated grouping problem in large RFID system. We start with a straightforward solution called the Enhanced Polling Grouping EPG protocol. We then propose a time-efficient Filter Grouping FIG protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping CCG protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. We further enhance CCG to make it perform better with very large groups. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.

In RFID systems, the grouping problem is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on the unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of m tags simultaneously in multicast, which improves the efficiency by m times when comparing with unicast. We study fast grouping protocols in large RFID systems. To the best of our knowledge, it is the first attempt to tackle this practically important yet uninvestigated problem. We start with a straightforward solution called the Enhanced Polling Grouping (EPG) protocol. We then propose a time-efficient FIltering Grouping (FIG) protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping (CCG) protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.

The benefit of offloading applications from smart-phones to cloud servers is undermined by the significant energy consumption in data transmission. Most previous approaches attempt to improve the energy efficiency only by choosing a more energy efficient network. However, we find that for computer vision applications, pre-processing the data before offloading can also substantially lower the energy consumption in data transmission at the cost of lower result accuracy. In this paper, we propose a novel online decision making approach to determining the pre-processing level for either higher result accuracy or better energy efficiency in a mobile environment. Different from previous work that maximizes the energy efficiency, our work takes the energy consumption as a constraint. Since people usually charge their smartphones daily, it is unnecessary to extend the battery life to last more than a day. Under both the energy and time constraints, we attempt to solve the problem of maximizing the result accuracy in an online way. Our real-world evaluation shows that the implemented prototype of our approach achieves a near-optimal accuracy for application execution results (nearly 99% correct detection rate for face detection), and sufficiently satisfies the energy constraint.

Summary Distributed data‐centric storage in wireless sensor networks (WSNs) is considered as a promising big data storage approach, because it contributes to reducing the communication overhead inside the networks. However, most of the existing distributed methods rely on locating systems, which consume more energy, and assume that sensors are uniformly distributed, which is clearly not applicable for the scenarios with nonuniform sensor distribution. To address these issues, in this paper, we propose a big data storage and retrieval algorithm for WSNs with nonuniform node distribution, which aims at estimating the real distribution and the addresses of sensor nodes. In particular, we consider the data redundancy among neighbor nodes in the proposed algorithm and exploit a simple routing based on the algorithm. Experimental results show that our approach outperforms other approaches in terms of data querying efficiency and data loss rate. Copyright © 2015 John Wiley & Sons, Ltd.

The radio frequency identification (RFID) technology is greatly revolutionizing applications such as warehouse management and inventory control in retail industry. In large RFID systems, an important and practical issue is tag searching: Given a particular set of tags called wanted tags, tag searching aims to determine which of them are currently present in the system and which are not. As an RFID system usually contains a large number of tags, the intuitive solution that collects IDs of all the tags in the system and compares them with the wanted tag IDs to obtain the result is highly time inefficient. In this paper, we design a novel technique called testing slot, with which a reader can quickly figure out which wanted tags are absent from its interrogation region without tag ID transmissions. The testing slot technique thus greatly reduces transmission overhead during the searching process. Based on this technique, we propose two protocols to perform time-efficient tag searching in practical large RFID systems containing multiple readers. In our protocols, each reader first employs the testing slot technique to obtain its local searching result by iteratively eliminating wanted tags that are absent from its interrogation region. The local searching results of readers are then combined to form the final searching result. The proposed protocols outperform existing solutions in both time efficiency and searching precision. Simulation results show that, compared with the state-of-the-art solution, our best protocol reduces execution time by up to 60 percent, meanwhile promotes the searching precision by nearly an order of magnitude.

This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative clustered wireless sensor networks, where energy-constrained relay nodes harvest the ambient radio-frequency (RF) signal and use the harvested energy to forward the packets from sources to destinations. To this end, we first formulate the energy-efficient cooperative transmission (eCotrans) problem for SWIPT in clustered wireless sensor networks as a non-convex constrained optimization problem. Then, by exploiting fractional programming and dual decomposition, we develop a distributed iteration algorithm for power allocation, power splitting and relay selection to solve the non-convex optimization problem. We find that power splitting ratio plays an imperative role in relay selection. Our simulation results illustrate that the proposed algorithm can converge within a few iterations and the numerical analysis provides practical insights into the effect of various system parameters, such as the number of relay nodes, the inter-cluster distance and the maximum transmission power allowance, on energy efficiency and average harvested power.

Cloud computing applications face the challenges of dealing with a huge volume of data that needs the support of accurate and fast approximate queries to enhance system scalability and improve quality of service. Locality-sensitive hashing (LSH) can support the approximate queries that unfortunately suffer from imbalanced load and space inefficiency among distributed data servers, which severely limits the query accuracy and incurs long query latency between users and cloud servers. In this paper, we propose a novel scheme, called NEST, which offers easy-to-use and cost-effective approximate queries for cloud computing. The novelty of NEST is to leverage cuckoo-driven locality-sensitive hashing to find similar items that are further placed closely through cuckoo-driven method to obtain load-balancing buckets in hash tables. NEST hence carries out flat and manageable addressing in adjacent buckets, and obtains constant-scale query complexity even in the worst case. The benefits of NEST include the increments of space utilization and fast query response. Moreover, due to the salient property of flat addressing in NEST, we implement NEST design in a real hybrid storage system, which consists of DRAM, SSD, and hard disk. The flat addressing allows efficient operations in SSD to improve system performance. We argue that a proper “division of labor” among DRAM, SSD, and hard disk in the hybrid and heterogeneous storage hierarchy is desperately needed to strike an optimal balance to remove the indexing bottleneck. Theoretical analysis and extensive experiments (on LANL and Microsoft metadata) in a large-scale cloud testbed demonstrate the salient properties of NEST to meet the needs of approximate query service in cloud computing environments. We have offered open-source codes of NEST for public use.

Radio-Frequency Identification (RFID) technology brings revolutionary changes to many fields like retail industry. One important research issue in large RFID systems is the identification of unknown tags, i.e., tags that just entered the system but have not been interrogated by reader(s) covering them yet. Unknown tag identification plays a critical role in automatic inventory management and misplaced tag discovery, but it is far from thoroughly investigated. Existing solutions either trivially interrogate all the tags in the system and thus are highly time inefficient due to re-identification of already identified tags, or use probabilistic approaches that cannot guarantee complete identification of all the unknown tags. In this paper, we propose a series of protocols that can identify all of the unknown tags with high time efficiency. We develop several novel techniques to quickly deactivate already identified tags and prevent them from replying during the interrogation of unknown tags, which avoids re-identification of these tags and consequently improves time efficiency. To our knowledge, our protocols are the first non-trivial solutions that guarantee complete identification of all the unknown tags. We illustrate the effectiveness of our protocols through both rigorous theoretical analysis and extensive simulations. Simulation results show that our protocols can save up to 70 percent time when compared with the best existing solutions.

In RFID-enabled applications, we may pay more attention to key tags instead of all tags. This paper studies the problem of key tag counting, which aims at estimating how many key tags in a given set exist in the current RFID system. Previous work is slow to solve this new problem because of the serious interference replies from the large number of ordinary (i.e., Nonkey) tags. However, time-efficiency is an important metric for the fast tag cardinality estimation in a large-scale RFID system. In this paper, we propose a singleton slot-based estimator, which is time-efficient because the RFID reader only needs to observe the status change of expected singleton slots of key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small for "key" members should be rare. As a result, even when the whole time frame is long, the expected singleton slot number is limited and the running of our protocol is fast to achieve estimation accuracy. Rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. We conduct simulations and implement a prototype of our protocol to verify its efficiency and deployability.

Radio-Frequency Identification (RFID) technology has fostered many object monitoring systems. Along with this trend, tagged objects' value and privacy become a primary concern. A corresponding important problem is to verify the intactness of a set of tagged objects without leaking tag identifiers (IDs). However, existing solutions necessitate the knowledge of tag IDs. Without tag IDs as a priori, this paper studies intactness verification in anonymous RFID systems. We identify three critical solution requirements, that is, deterministic verification, anonymity preservation, and scalability. We propose Cardiff and Divar, two crypto-free, lightweight protocols that isolate tag IDs from intactness verification and satisfy solution requirements. Cardiff explores tag cardinality as intactness proof while Divar leverages Direct-Sequence Spread Spectrum (DSSS) enabled RFID. Both analytical and simulation results demonstrate that Cardiff and Divar can satisfy the requirements of accuracy, privacy, and scalability.

Radio Frequency Identification (RFID) has dramatically streamlined supply chain management by automatically monitoring and tracking commodities. Considering the proliferation of RFID data volume, distributed storage is more applicable and scalable than centralized storage for distributed query processing. Traditional distributed RFID data storage requires each distribution center to locally store raw RFID data, leading to data redundancy, storage and query inefficiency. In this paper, we design an efficient distributed storage model by leveraging Bloom filters to save storage space and improve query efficiency. Meanwhile, we establish corresponding query processing schemes to locally support existence queries and path queries, which are two kinds of most popular queries in the supply chain management. A local query can be completed with constant time complexity regardless of data volume. Experiments demonstrate that our storage model outperforms the traditional one in terms of both space and time efficiency.

Tag identification is the most fundamental problem in Radio Frequency Identification (RFID) systems. Time efficiency is the top quality of service (QoS) metric in RFID tag identification. Traditional tag scanning approaches suffer from low time efficiency because they need to transmit tag IDs that are usually very long (e.g., 96 bits). In this paper, we investigate how to employ handheld readers to improve the time efficiency of tag identification and provide flexibility to scan tags on different purposes. A fast and flexible tag scanning mechanism called LOCK is proposed, which combines both the information and the replying slot index of a tag's response. In LOCK, tags transmit only short responses instead of tag IDs. Based on LOCK, we propose two novel tag scanning protocols that progressively add new techniques on top of one another to improve the time efficiency. Compared to the state-of-the-art solution in literature, our best protocol reduces scanning time by up to 53 percent.

BitTorrent (BT) is one of the most common Peer-to-Peer (P2P) file sharing protocols. Rather than downloading a file from a single source, the protocol allows users to join a swarm of peers to download and upload from each other simultaneously. Worms exploiting information from BT servers or trackers can cause serious damage to participating peers, which unfortunately has been neglected previously. In this article, we first present a new worm, called Adaptive BitTorrent worm (A-BT worm), which finds new victims and propagates sending forged requests to trackers. To reduce its abnormal behavior, the worm estimates the ratio of infected peers and adaptively adjusts its propagation speed. We then build a hybrid model to precisely characterize the propagation behavior of the worm. We also propose a statistical method to automatically detect the worm from the tracker by estimating the variance of the time intervals of requests. To slow down the worm propagation, we design a safe strategy in which the tracker returns secured peers when receives a request. Finally, we evaluate the accuracy of the hybrid model, and the effectiveness of our detection method and containment strategy through simulations.

In wireless sensor networks, an important research problem is to use a few anchor nodes with known locations to derive the locations of other nodes deployed in the sensor field. A category of solutions for this problem is the iterative localization, which sequentially merges the elements in a network to finally locate them. Here, a network element is different from its definition in iterative trilateration. It can be either an individual node or a group of nodes. For this approach, we identify a new problem called inflexible body merging, whose objective is to align two small network elements and generate a larger element. It is more generalized than the traditional tools of trilateration and patch stitching and can replace them as a new merging primitive. We solve this problem and make the following contributions. Our primitive can tolerate ranging noise when merging two network elements. It adopts an optimization algorithm based on rigid body dynamics and relaxing springs. Our primitive improves the robustness against flip ambiguities. It uses orthogonal regression to detect the rough collinearity of nodes in the presence of ranging noise, and then enumerate flip ambiguities accordingly. We present a condition to indicate when we can apply this primitive to align two network elements. This condition can unify previous work and thus achieve a higher percentage of localizable nodes. All the declared contributions have been validated by both theoretical analysis and simulation results.

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.

Blocker tags are initially introduced to protect regular tags in certain ID ranges, called blocking ranges, from unwanted scanning in RFID systems. But if misused, blocker tags can cause blocking attacks that corrupt the communication between interfered regular tags and readers. Previous approaches can only detect blocking behavior. However, they cannot distinguish malicious blocking from legitimate blocking that can be perfectly allowed to protect customer's privacy. To solve the problem, we carry out the first attempt in the paper to detect real blocking attacks by identifying malicious blocking ranges from authorized ones in a system. We present two pioneer probe-based protocols that can accurately identify malicious blocking ranges in popular tree-based RFID systems, and get rid of their impact before performing RFID applications. We validate the efficacy of the two protocols through theoretical analysis and simulation experiments. The results show that our protocols can identify blocking ranges very fast even when the blocker tag percentage is very low, for example, dozens of blocker tags among tens of thousands of regular tags. Our protocols deliver also a faster blocker tag detection than previous detection methods; our best protocol reduces detection time by over 90% compared with the state-of-the-art detection method.

Radio Frequency Identification (RFID) technology has been widely applied in many scenarios such as inventory control, supply chain management due to its superior properties including fast identification and relatively long interrogating range over barcode systems. It is critical to efficiently identify the unknown tags because these tags can appear when new tagged objects are moved in or wrongly placed. The state-of-the-art Basic Unknown tag Identification Protocol-with Collision-Fresh slot paring (BUIP-CF) protocol can first deactivate all the known tags and then collect all the unknown tags. However, BUIP-CF protocol investigates an ALOHA-like technique and causes too many tag responses, which results in low efficiency. This paper proposes a Fast Unknown tag Identification (FUI) protocol which investigates an indicator vector to label the unknown tags with a given accuracy and removes the time-consuming tag responses in the deactivation phase. FUI also adopts the classical Enhanced Dynamic Framed Slotted ALOHA (EDFSA) protocol to collect the labeled unknown tags. We then investigate the optimal parameter settings to maximize the performance of the proposed FUI protocol. Extensive simulation experiments are conducted to evaluate the performance of the proposed FUI protocol and the experimental results show that it considerably outperforms the state-of-the-art protocol.

Cloud computing applications face the challenges of dealing with a huge volume of data that needs the support of fast approximate queries to enhance system scalability and improve quality of service, especially when users are not aware of exact query inputs. Locality-Sensitive Hashing (LSH) can support the approximate queries that unfortunately suffer from imbalanced load and space inefficiency among distributed data servers, which severely limits the query accuracy and incurs long query latency between users and cloud servers. In this paper, we propose a novel scheme, called NEST, which offers ease-of-use and cost-effective approximate query service for cloud computing. The novelty of NEST is to leverage cuckoo-driven locality-sensitive hashing to find similar items that are further placed closely to obtain load-balancing buckets in hash tables. NEST hence carries out flat and manageable addressing in adjacent buckets, and obtains constant-scale query complexity even in the worst case. The benefits of NEST include the increments of space utilization and fast query response. Theoretical analysis and extensive experiments in a large-scale cloud testbed demonstrate the salient properties of NEST to meet the needs of approximate query service in cloud computing environments.

Efficient estimation of tag population in RFID systems has many important applications. In this paper, we present a new problem called differential cardinality estimation, which tracks the population changes in a dynamic RFID system where tags are frequently moved in and out. In particular, we want to provide quick estimation on (1) the number of new tags that are moved in and (2) the number of old tags that are moved out, between any two consecutive scans of the system. We show that the traditional cardinality estimators cannot be applied here, and the tag identification protocols are too expensive if the estimation needs to be performed frequently in order to support real-time monitoring. This paper presents the first efficient solution for the problem of differential cardinality estimation. The solution is based on a novel differential estimation framework, and is named zero differential estimator. We show that this estimator can be configured to meet any pre-set accuracy requirement, with a probabilistic error bound that can be made arbitrarily small.

Radio-Frequency Identification (RFID) technology has successfully proven its potential for locating objects in a 3-dimensional (3D) space. Current RFID-based 3D localization is built on the ethos of striving for accuracy. This paper takes the first step toward efficient localization with high time efficiency and energy efficiency, which are important for accelerating positioning operation and prolonging system lifetime. To this end, we propose leveraging known locations of deployed reference readers and reference tags to probe as a few reference tags as sufficient for localization. Counter-intuitively, localization using fewer reference tags promises rather higher efficiency yet without necessarily sacrificing accuracy. We design efficient passive scheme and efficient active scheme for both typical RFID-based 3D localization scenarios, locating a target tag using reference readers/tags and locating a target reader using reference tags. We evaluate their performance through quantitative analysis and extensive simulation. The results show that the proposed schemes outperform existing schemes in time efficiency and energy efficiency by over 95% on average.

SUMMARY As radio frequency identification (RFID) tags become more ubiquitously available, they will stay in dynamic environments where tags can freely enter or leave RFID readers' interrogation range. With such a dynamic tag population, there arises a problem of population monitoring, whose purpose is to identify themissing tagsthat have departed from the reading range and thenew tagsthat have newly entered. This problem is a new problem which cannot be well solved by the conventional tag identification protocols. In this paper, we first show that this traditional approach is inefficient, because it collects all the tag IDs in each scan and ignores the ready‐for‐use knowledge of the tag population in a previous scan. To be more efficient, we present three protocols: (i) a baseline protocol that improves the traditional tag identification protocol by optimizing its length of random number used for collision detection; (ii) a novel one‐phase protocol with easy labor to identify exactly the new tags and the missing tags by fully utilizing the knowledge of previous tag population; and (iii) a hybrid protocol that smartly combines the baseline protocol and the one‐phase protocol. Its purpose is to deal with the situation that the knowledge of previous tag population is highly inconsistent with the current tag population. This hybrid protocol, as shown by our analysis, can improve the tag monitoring accuracy by 25%, and improve the time efficiency by 55.3%, as compared with a recent work (called two‐phase protocol), which also identifies the population changes. Copyright © 2012 John Wiley & Sons, Ltd.

Cloning attacks threaten radio-frequency identification (RFID) applications but are hard to prevent. Existing cloning attack detection methods are enslaved to the knowledge of tag identifiers (IDs). Tag IDs, however, should be protected to enable and secure privacy-sensitive applications in anonymous RFID systems. In a first step, this paper tackles cloning attack detection in anonymous RFID systems without requiring tag IDs as a priori. To this end, we leverage unreconciled collisions to uncover cloning attacks. An unreconciled collision is probably due to responses from multiple tags with the same ID, exactly the evidence of cloning attacks. This insight inspires GREAT, our pioneer protocol for cloning attack detection in anonymous RFID systems. We evaluate the performance of GREAT through theoretical analysis and extensive simulations. The results show that GREAT can detect cloning attacks in anonymous RFID systems fairly fast with required accuracy. For example, when only six out of 50,000 tags are cloned, GREAT can detect the cloning attack in 75.5 s with a probability of at least 0.99.

In wireless sensor networks, a critical system service is the localization service that determines the locations of geographically distributed sensor nodes. The raw data used by this service are the distance measurements between neighboring nodes and the position knowledge of anchor nodes. However, these raw data may contain outliers that strongly deviate from their true values, which include both the outlier distances and the outlier anchors. These outliers can severely degrade the accuracy of the localization service. Therefore, we need a robust localization algorithm that can reject these outliers. Previous studies in this field mainly focus on enhancing multilateration with outlier rejection ability, since multilateration is a primitive operation used by localization service. But patch merging, a powerful operation for increasing the percentage of localizable nodes in sparse networks, is almost neglected. We thus propose a robust patch merging operation that can reject outliers for both multilateration and patch merging. Based on this operation, we further propose a robust network localization algorithm called RobustLoc . This algorithm makes two major contributions. (1) RobustLoc can achieve a high percentage of localizable nodes in both dense and sparse networks. In contrast, previous methods based on robust multilateration almost always fail in sparse networks with average degrees between 5 and 7. Our experiments show that RobustLoc can localize about 90% of nodes in a sparse network with 5.5 degrees. (2) As far as we know, RobustLoc is the first to uncover the differences between outlier distances and outlier anchors. Our simulations show that RobustLoc can reject colluding outlier anchors reliably in both convex and concave networks.

Piece-related algorithms, including piece revelation, selection, and queuing, play a crucial role in the BitTorrent (BT) protocol, because the BT system can be viewed as a market where peers trade their pieces with one another. During the piece exchanging, a peer selects some pieces revealed by neighbors, and queues them up for downloading. In this paper, we provide a deep understanding of these algorithms, and also propose some improvements to them. Previous study has shown that the piece revelation strategy is vulnerable to under-reporting. We provide a game-theoretic analysis for this selfish gaming, and propose a distributed credit method to prevent it. Existing piece selection strategies, though long believed to be good enough, may fail to balance piece supply and demand. We propose a unified strategy to shorten the download time of peers by applying utility theory. The design of the piece queuing algorithm has a conflict with that of piece selection strategy, because it is not possible to assume that the queued requests for a selected piece can always be available on multiple neighbors. We give a possible fix to address the conflict by allowing peers to dynamically manage their unfulfilled requests. To evaluate the performance of the proposed algorithms, we run several experiments in a live swarm. Our primary results show that they can achieve fast individual and system-wide download time.

In this paper, we will address the modeling and optimal design of linear network coding (LNC) for secure unicast with multiple streams between the same source and destination pair. The objectives include 1) satisfying the weakly secure requirements, 2) maximizing the transmission data rate, and 3) minimizing the size of the finite field. To fulfill the first two objectives, we formulate a secure unicast routing problem and prove that it is equivalent to a constrained link-disjoint path problem. Based on this fact, we develop an efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. With the given topology, we investigate the design of both weakly secure deterministic LNC and weakly secure random LNC. In the designs of deterministic LNC and random LNC, we prove that the required size of the finite field decreases with the decrease of the number of intermediate nodes in the topology. Therefore, to meet the third objective, we formulate a problem to minimize the number of intermediate nodes. We prove that this problem is NP-Complete and develop an approximation algorithm to solve it. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

Tag cloning attacks threaten a variety of Radio Frequency Identification (RFID) applications but are hard to prevent. To secure RFID applications that confine tagged objects in the same RFID system, this paper studies the cloned-tag identification problem. Although limited existing work has shed some light on the problem, designing fast cloned-tag identification protocols for applications in large-scale RFID systems is yet not thoroughly investigated. To this end, we propose leveraging broadcast and collisions to identify cloned tags. This approach relieves us from resorting to complex cryptography techniques and time-consuming transmission of tag IDs. Based on this approach, we derive a time lower bound on cloned-tag identification and propose a suite of time-efficient protocols toward approaching the time lower bound. The execution time of our protocol is only 1.4 times the value of the time lower bound, being up to 91% less than that of the existing protocol. The proposed protocols may benefit also RFID applications that distribute tagged objects across multiple places.

The RFID technology greatly improves efficiency of many applications including inventory control, object tracking, and supply chain management. In such applications, it is common that new objects are added into the system or existing objects are misplaced in wrong regions. When this happens, fast and complete identification of such tags is very important. We name this problem unknown tag identification, as these tags appear to be unknown by the reader(s) currently covering them. In this paper, we propose a series of protocols to identify unknown tags completely and fast. In these protocols, we develop several novel techniques to efficiently resolve collisions caused by known tags when identifying unknown tags, which greatly improve the time efficiency. To our knowledge, this is the first work that completely identify all the unknown tags with deterministic approaches. Simulation results show the superior performance of the proposed protocols: Compared with a baseline method which collects IDs of all the tags in the system, our best protocol reduces the execution time by 63% in average and by 85% at most.

Flooding based Distributed Denial of Service (DDoS) attacks can cause very serious security problem by exhausting computing and bandwidth resources of victims. To mitigate these destructive attacks, it is crucially important to detect the occurrence of DDoS attacks and identify their targets as early as possible. In this paper, we propose a collaborative DDoS detection system, called VicSifter, which can detect ongoing DDoS attacks and identify victims at an early stage with good scalability and low overhead. VicSifter is deployed over multiple nodes with two kinds of functions: local anomaly detection and collaborative victim identification. The anomaly detection method is performed locally and is lightweight to save computation by measuring passing packets in a sketch. The collaborative victim identification is triggered only when a local anomaly is detected by employing our distinctive elimination mechanism. The mechanism can significantly reduce the number of packets to be processed by each node, making our system scalable for high-speed network links. We evaluate the performance of VicSifter by using real-world data traffic, mixing the real DDoS attack traces with captured campus gateway traffic. The results show that our system has high accuracy in the early detection of DDoS attacks and timely identification of targeted victims. Our system can outperform other existing methods with less space requirement, and thus achieving good system scalability.

In many network applications, Bloom filters are used to support exact-matching membership query for their randomized space-efficient data structure with a small probability of false answers. In this paper, we extend the standard Bloom filter to Locality-Sensitive Bloom Filter (LSBF) to provide Approximate Membership Query (AMQ) service. We achieve this by replacing uniform and independent hash functions with locality-sensitive hash functions. Such replacement makes the storage in LSBF to be locality sensitive. Meanwhile, LSBF is space efficient and query responsive by employing the Bloom filter design. In the design of the LSBF structure, we propose a bit vector to reduce False Positives (FP). The bit vector can verify multiple attributes belonging to one member. We also use an active overflowed scheme to significantly decrease False Negatives (FN). Rigorous theoretical analysis (e.g., on FP, FN, and space overhead) shows that the design of LSBF is space compact and can provide accurate response to approximate membership queries. We have implemented LSBF in a real distributed system to perform extensive experiments using real-world traces. Experimental results show that LSBF, compared with a baseline approach and other state-of-the-art work in the literature (SmartStore and LSB-tree), takes less time to respond AMQ and consumes much less storage space.

Radio-Frequency Identification (RFID) technology brings many innovative applications. Of great importance to RFID applications in production economics is misplaced-tag pinpointing (MTP), because misplacement errors fail optimal inventory placement and thus significantly decrease profit. The existing MTP solution [1], originally proposed from a data-processing perspective, collects and processes a large amount of data. It suffers from time inefficiency (and energy-inefficiency as well if active tags are in use). The problem of finding efficient solutions for the MTP problem from the communication protocol design perspective has never been investigated before. In this paper, we propose a series of protocols toward efficient MTP solutions in large RFID systems. The proposed protocols detect misplaced tags using reader positions instead of tag positions to guarantee the efficiency and scalability as system scale grows, because RFID readers are much fewer than tags. Considering applications that employ active tags, we further propose a solution requiring responses from only a subset of tags in favor of energy saving. We also design a distributed protocol that enables each reader to independently detect misplaced tags. We then investigate how to apply the proposed protocols in scenarios with tag mobility. To evaluate the proposed protocols, we analyze their optimal performances to demonstrate their efficiency potential and also conduct extensive simulation experiments. The results show that the proposed protocols can significantly increase the time efficiency and the energy efficiency by over 70 percent on average when compared with the best existing work.

As RFID tags become more ubiquitously available, e.g., in a supermarket, it is necessary to monitor larger-scale tag populations in a dynamic environment to get updated tag information. This paper considers the problem of monitoring a dynamic tag population, to identify both the missing tags and new tags. Traditional approach can solve the problem by collecting all tag IDs in the current population, which could be slow because it ignores the knowledge of the tag population in a previous scan. To be more efficient, this paper presents two protocols: (1) a baseline protocol with optimized length of random number bits, (2) an improved one-phase protocol with easy labor to identify only the new and missing tags in ALOHA frames by fully utilizing previous tag population knowledge. Our analysis shows that the one-phase protocol can improve the monitoring accuracy by 25% and improve the time efficiency by 55%, as compared with the two-phase protocol proposed in a recent paper which also identifies population changes.

With the development of various wireless personal area networks (WPANs), the issue of security has become a crucial problem for their applications. Jamming is one of the most important methods of attack to deprive or reduce the communication service of WPANs. Most existing jamming attacks can cause negative interference, but the attack strategies are usually not adjusted against the countermeasures that are currently taken. This paper proposes an effective dynamic jamming attack (EDJam) in an 802.15.4-compliant WPAN. In this attack, a jammer who is aware of a change in the network defense strategy, e.g. the use of a dynamic retransmission mechanism, may choose a better strategy to make more damage to the network with less cost. Similarly, a well-protected network can change its defense strategy against the EDJam. This procedure of competition between the EDJam attacker and defending networks is modeled and formulated as a Stackelberg game, and a unique Nash Equilibrium point is derived in analytical format. Based on an equilibrium analysis, we discuss the condition under which a defense strategy will increase the utility of the network and a dynamic retransmission mechanism defense strategy is proposed accordingly. The simulation results show that EDjam can be more cost-efficient than continuous, random and fixed-period jamming.

Similar to the revolutionary change that the barcode system brought to the retail industry, the RFID technologies are expected to revolutionize the warehouse and inventory management. After RFID tags are deployed to make the attached objects wirelessly identifiable, a natural next step is to invent new ways to benefit from this “infrastructure”. For example, sensors may be added to these tags to gather real-time information about the state of the objects or about the environment where these objects reside. This leads to the problem of designing efficient protocols to collect such information from the tags. It is a new problem that the existing work cannot solve well. In this paper, we first show that a straightforward polling solution will not be efficient. We then propose a single-hash information collection protocol that works much better than the polling solution. However, a wide gap still exists between the execution time of this protocol and a lower bound that we establish. Finally, we propose a multi-hash information collection protocol that further reduces the expected execution time to within 1.61 times the lower bound.

In this paper, we study the optimal design of linear network coding (LNC) for secure unicast against passive attacks, under the requirement of information theoretical security (ITS). The objectives of our optimal LNC design include (1) satisfying the ITS requirement, (2) maximizing the transmission rate of a unicast stream, and (3) minimizing the number of additional random symbols. We first formulate the problem that maximizes the secure transmission rate under the requirement of ITS, which is then transformed to a constrained maximum network flow problem.We devise an efficient algorithm that can find the optimal transmission topology. Based on the transmission topology, we then design a deterministic LNC which satisfies the aforementioned objectives and provide a constructive upper bound of the size of the finite field. In addition, we also study the potential of random LNC and derive the low bound of the probability that a random LNC is information theoretically secure.

The Radio-Frequency Identification (RFID) technology has stimulated many innovative applications. Misplaced-tag pinpointing (MTP) is important to RFID applications in production economics because optimal inventory placement can significantly increase profit. Previous research from the database perspective needs to process a large amount of data which is time-consuming to collect (and energy-consuming if active tags are used). How to efficiently address the MTP problem from the protocol design perspective however has not been investigated. In this paper, we propose a series of protocols toward efficient MTP solution in large RFID systems. The proposed protocols detect misplaced tags based on reader positions instead of tag positions to guarantee the efficiency and scalability as system scale grows, because the number of readers is much smaller than that of tags. Considering applications to employ more and more popular active tags, we further propose a solution requiring responses from only partial tags in favor of energy saving. We analyze the optimal performances of proposed protocols to demonstrate their efficiency potential and conduct extensive simulation experiments to evaluate their performance under various scenarios. The results show that the proposed protocols can significantly increase the time efficiency and the energy efficiency by over 70% on average when compared with the state of the art.

Distance estimation is a key issue in range-free localization algorithms for wireless sensor networks. Approaches that assume isotropy of networks, such as Dv-hop and Gradient, cannot obtain accurate distance estimations in anisotropic sensor networks thus are not applicable to such networks. The anisotropy of sensor networks comes from two aspects: uneven nodal distribution and irregularity of deployment region. Existing localization algorithms for anisotropic wireless sensor networks usually only deal with one of the two aspects. In this paper, we propose an anchor supervised distance estimation approach which can simultaneously cope with both of the two aspects. In this approach, an anchor node selects a “friendly” subset from all other anchor nodes to which its distance estimates are accurate and broadcasts the selection result to neighboring common nodes. The common nodes then use these friendly anchors to perform distance estimation. We analyze distance estimation accuracy of this approach through extensive simulations. The results show that, compared with Dv-hop, our proposed approach dramatically reduces distance estimation error in anisotropic wireless sensor networks with an average factor of 67%. Consequently, the localization error of Dv-hop is reduced by an average factor of 71% if enhanced with our distance estimation approach.

Network-based signature generation (NSG) has been proposed as a way to automatically and quickly generate accurate signatures for worms, especially polymorphic worms. In this paper, we propose a new NSG system-PolyTree, to defend against polymorphic worms. We observe that signatures from worms and their variants are relevant and a tree structure can properly reflect their familial resemblance. Hence, in contrast to an isolated view of generated signatures in previous approaches, PolyTree organizes signatures extracted from worm samples into a tree structure, called signature tree, based on the formally defined "more specific” relation of simplified regular expression signatures. PolyTree is composed of two components, signature tree generator and signature selector. The signature tree generator implements an incremental signature tree generation algorithm from worm sample clustering, up-to-date signature refinement to efficient tree construction. The incremental signature tree construction gives insight on how the worm variants evolve over time and allows signature refinement upon a new worm sample arrival. The signature selector chooses a set of signatures for worm detection from a benign traffic pool and the current signature tree constructed by the signature tree generator. Experiments show that PolyTree cannot only generate accurate signatures for polymorphic worms with noise, but these signatures are well organized in the signature tree to reflect the inherent relations of worms and their variants.

This paper proposes an algorithm called Imprecise Spectrum Analysis (ISA) to carry out fast dimension reduction for document classification. ISA is designed based on the one-sided Jacobi method for Singular Value Decomposition (SVD). To speedup dimension reduction, it simplifies the orthogonalization process of Jacobi computation and introduces a new mapping formula for transforming original document-term vectors. To improve classification accuracy using ISA, a feature selection method is further developed to make inter-class feature vectors more orthogonal in building the initial weighted term-document matrix. Our experimental results show that ISA is extremely fast in handling large term-document matrices and delivers better or competitive classification accuracy compared to SVD-based LSI.

Linear network coding is a promising technology that can maximize the throughput capacity of communication network. Despite this salient feature, there are still many challenges to be addressed, and security is clearly one of the most important challenges. In this paper, we will address the design of secure linear network coding. Specifically, we will investigate the network coding design that can both satisfy the weakly secure requirements and maximize the transmission data rate of multiple unicast streams between the same source and destination pair, which has not been addressed in the literature. In our study, we first prove that the secure unicast routing problem is equivalent to a constrained link-disjoint path problem. We then develop efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. Based on the topology, we design deterministic linear network code that is weakly secure and can be constructed at the source node. And finally, we investigate the potential of random linear code for weakly secure unicast and prove the low bound of the probability that a random linear code is weakly secure.

Bootstrapping is critical in any P2P network, since, on initial startup, a peer must bootstrap and find at least one neighbor. Existing P2P networks simply rely on centralized servers or static peers for bootstrapping, which may become a single point of failure. Recently, the shutdown of BT web-sites in China has caused a serious problem in BT bootstrapping. A decentralized way to bootstrap P2P networks is to clone existing social networks. In particular, a new peer obtains addresses of potential neighbors by sniffing instant messaging packets (e.g., MSN or QQ packets). With such addresses, the peer can bootstrap neighbors and join the network without the help of any centralized server.

Localization is a fundamental problem in wireless sensor networks and its accuracy impacts the efficiency of location-aware protocols and applications, such as routing and storage. Most previous localization algorithms assume that sensors are distributed in regular areas without holes or obstacles, which often does not reflect real-world conditions, especially for outdoor deployment of wireless sensor networks. In this paper, we propose a novel scheme called reliable anchor-based localization (RAL), which can greatly reduce the localization error due to the irregular deployment areas. We first provide theoretical analysis of the minimum hop length for uniformly distributed networks and then show its close approximation to empirical results, which can assist in the construction of a reliable minimal hop-length table offline. Using this table, we are able to tell whether a path is severely detoured and compute a more accurate average hop length as the basis for distance estimation. At runtime, the RAL scheme 1) utilizes the reliable minimal hop length from the table as the threshold to differentiate between reliable anchors and unreliable ones, and 2) allows each sensor to determine its position utilizing only distance constraints obtained from reliable anchors. The simulation results show that RAL can effectively filter out unreliable anchors and therefore improve the localization accuracy.

This paper focuses on multihop range-free localization in anisotropic wireless sensor networks. In anisotropic networks, geometric distance between a pair of sensor nodes is not always proportional to their hop count distance, which undermines the assumption of many existing range-free localization algorithms. To tolerate network anisotropy, we propose a pattern-driven localization scheme, which is inspired by the observation that in an anisotropic network the hop count field propagated from an anchor exhibits multiple patterns, under the interference of multiple anisotropic factors. Our localization scheme therefore for different patterns adopts different anchor-sensor distance estimation algorithms. The average anchor-sensor distance estimation accuracy of our scheme, as demonstrated by both theoretical analysis and extensive simulations, is improved to be better than 0.4r when the average sensor density is above eight, and the sensor localization accuracy thus is approximately better than 0.5r. This localization accuracy can satisfy the needs of many location-dependent protocols and applications, including geographical routing and tracking. Compared with previous localization algorithms that declares to tolerate network anisotropy, our localization scheme excels in 1) higher accuracy stemming from its ability to tolerate multiple anisotropic factors, including the existence of obstacles, sparse and nonuniform sensor distribution, irregular radio propagation pattern, and anisotropic terrain condition, 2) localization accuracy guaranteed by theoretical analysis, rather than merely by simulations, and 3) a distributed solution with less communication overhead and enhanced robustness to different network topologies.

One widely used mechanism for representing membership of a set of items is the simple space-efficient randomized data structure known as Bloom filters. Yet, Bloom filters are not entirely suitable for many new network applications that support network services like the representation and querying of items that have multiple attributes as opposed to a single attribute. In this paper, we present an approach to the accurate and efficient representation and querying of multiattribute items using Bloom filters. The approach proposes three variant structures of Bloom filters: parallel Bloom filter (referred as PBF) structure, PBF with a hash table (PBF-HT), and PBF with a Bloom filter (PBF-BF). PBF stores multiple attributes of an item in parallel Bloom filters. The auxiliary HT and BF provide functions to capture the inherent dependency of all attributes of an item. Compared to standard Bloom filters to represent items with multiple attributes, the proposed PBF facilitates much faster query service and both PBF-HT and PBF-BF structures achieve much lower false positive probability with a result to save storage space. Simulation and experimental results demonstrate that the new space-efficient Bloom filter structures can efficiently and accurately represent multiattribute items and quickly respond queries at the cost of a relatively small false positive probability.

Worm propagation analysis, including exploring mechanisms of worm propagation and formulating effects of network/worm parameters, has great importance for worm containment and host protection in P2P networks. Previous work only focuses on topological worm propagation where worms search a hosts neighbor-list to find new victims. In BitTorrent (BT) networks, the information from servers or trackers, however, could be fully exploited to design effective worms. In this paper, we propose a new approach for worm propagation in BT-like P2P networks. The worm, called Dynamic Hit-List (DHL) worm, locates new victims and propagates itself by requesting a tracker to build a dynamic hit list, which is a self-stopping BT worm to be stealthy. We construct an analytical model to study the propagation of such a worm: breadth-first propagation and depth-first propagation. The analytical results provide insights of the worm design into choosing parameters that enable the worm to stop itself after compromising a large fraction of vulnerable peers in a P2P network. We finally evaluate the performance of DHL worm through simulations. The simulation results verify the correctness of our model and show the effectiveness of the worm by comparing it with the topological worm.

The InternationalWorkshop on Security in Systems and Networks is a forum for the presentation and discussion of approaches, research findings, and experiences in the area of privacy, integrity, and availability of resources in distributed systems. This workshop aims to bring together the technologies and researchers who share interest in the area of network and distributed system security. The main purpose is to promote discussions of research and relevant activities in security-related subjects. It also aims at increasing the synergy between academic and industry professionals working in this area.

This paper deals with the problem of guiding mobile sensors (or robots) to a phenomenon across a region covered by static sensors. We present a distributed, reliable and energy-efficient algorithm to construct a smoothed moving trajectory for a mobile robot. The reliable trajectory is realized by first constructing among static sensors a distributed hop count based artificial potential field (DH-APF) with only one local minimum near the phenomenon, and then navigating the robot to that minimum by an attractive force following the reversed gradient of the constructed field. Besides the attractive force towards the phenomenon, our algorithm adopts an additional repulsive force to push the robot away from obstacles, exploiting the fast sensing devices carried by the robot. Compared with previous navigation algorithms that guide the robot along a planned path, our algorithm can (1) tolerate the potential deviation from a planned path, since the DH-APF covers the entire deployment region; (2) mitigate the trajectory oscillation problem; (3) avoid the potential collision with obstacles; (4) save the precious energy of static sensors by configuring a large moving step size, which is not possible for algorithms neglecting the issue of navigation reliability. Our theoretical analysis of the above features considers practical sensor network issues including radio irregularity, packet loss and radio conflict. We implement the proposed algorithm over TinyOS and test its performance on the simulation platform with a high fidelity provided by TOSSIM and Tython. Simulation results verify the reliability and energy efficiency of the proposed mobile sensor navigation algorithm.

Multidimensional data indexing has received much research attention recently in a centralized system. However, it remains a nascent area of research in providing an integrated structure for multiple queries on multidimensional data in a distributed environment. In this paper, we propose a new data structure, called BR-tree (Bloom-filter-based R-tree), and implement such a prototype in the context of a distributed system. The node in a BR-tree, viewed as an expansion from the traditional R-tree node structure, incorporates space-efficient Bloom filters to facilitate fast membership queries. The proposed BR-tree can simultaneously support not only existing point and range queries, but also cover and bound queries that can potentially benefit various data indexing services. Compared with previous data structures, BR-tree achieves space efficiency and provides quick response (lesO(log n)) on these four types of queries. Our extensive experiments in a distributed environment further validate the practicality and efficiency of the proposed BR-tree structure.

Recent work in building data-centric sensor networks treats a sensor network as a pool of information. Sensor nodes generate, store, and retrieve information as both data producers and consumers. The intensive demand of data exchange within the network leads previous approaches with sensor-to-sink transmission model inefficient. In-network data storage schemes, such as geographical hash table (GHT) and double-rulings, have been accordingly proposed to structure the information storage among the network so as to facilitate the consumers to efficiently discover and retrieve data. Under those approaches, however, each sensor node needs to publish and retrieve data from different routes calculated every time, introducing unnecessary computation and communication overhead. This paper proposes anew approach that stores and queries data through bouncing tracks. Sensors are able to publish replica of generated data along their bouncing tracks and successfully retrieve data from other sensors along the same tracks, which largely simplifies data exchange and improves efficiency. The strengths of this design also include distance-bounded data retrieval. We conducted extensive simulations and the results show that this approach outperforms existing designs, including rumor routing and double-rulings, in terms of communication efficiency and cost.

Wireless sensor networks become ubiquitous to collect people's information in many people-centric applications, such as, health care, smart space and public safety. Because any misusage of these personal data might result in the leakage of privacy, it is expected that the data requesters can only access to the data what they are entitled to read. Based on a revised hash chain technique, we proposed a novel time-based privacy protection (TPP) scheme for multi-attribute data in WSNs. In the scheme, all the personal data are divided into 2-D subspaces representing data attribute and generation time. Data in each subspace is encrypted with a sub-key before its transmission to the sink. Anyone who wants to read data attribute at a particular time must get the corresponding sub-key from the sender node. TPP can generate a sub-key for data in each subspace in an efficient manner in terms of less sub-key generation time and low memory space usage. The simulation results show that the schemes can be applied to the resource limited WSNs efficiently.

Similarity search has been widely studied in peer-to-peer environments. In this paper, we propose the Bounded Locality Sensitive Hashing (Bounded LSH) method for similarity search in P2P file systems. Compared to the basic Locality Sensitive Hashing (LSH), Bounded LSH makes improvement on the space saving and quick query response in the similarity search, especially for high-dimensional data objects that exhibit non-uniform distribution property. We present simple and space-efficient Bounded-LSH to map non-uniform data space into load-balanced hash buckets that contain approximate number of objects. Load-balanced hash buckets in Bounded-LSH, in turn, require less number of hash tables while maintaining a high probability of returning the closest objects to requests. Our experiments based on synthetic and real-world datasets showed the feasibility, query and space efficiency of our proposed method.

The localization of sensor nodes is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from GPS devices or by virtue of being manually placed. In this paper, we propose a distributed method to localization of sensor nodes using a single moving beacon, where sensor nodes compute their position estimate based on the range-free technique. Two parameters are critical to the location accuracy of sensor nodes: the radio transmission range of the beacon and how often the beacon broadcasts its position. Theoretical analysis shows that these two parameters determine the upper bound of the estimation error when the traverse route of the beacon is a straight line. We extend the position estimate when the traverse route of the beacon is randomly chosen in a real-world situation, where the radio irregularity might cause a node to miss some crucial coordinate information from the beacon. We further point out that the movement pattern of the beacon plays a pivotal role in the localization task for sensors. To minimize estimation errors, sensor nodes can carry out a variety of algorithms in accordance with the movement of the beacon. Simulation results compare variants of the distributed method in a variety of testing environments. Real experiments show that the proposed method is feasible and can estimate the location of sensor nodes accurately, given a single moving beacon.

The localization of the sensor node is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from a GPS device or by virtue of being manually placed. In this paper, we propose two distributed methods to localization of sensor nodes using a single moving beacon where sensor nodes compute their position estimate based on the range-free technique. The first method uses the arrival and departure information of a walking beacon and the second method exploits the variance of the received signal strength (RSS) from the beacon. We provide the upper bound of the estimation error for these methods in an ideal environment. Critical to the location accuracy of sensor nodes are two more parameters, the radio transmission range of the beacon, and how often the beacon broadcasts its position. Simulation results show the location estimate error of sensor nodes applying the proposed two methods. The results are consistent to the theoretical analysis and the average estimate errors could be within one meter.

Selective forwarding attacks may corrupt some mission-critical applications such as military surveillance and forest fire monitoring. In these attacks, malicious nodes behave like normal nodes in most time but selectively drop sensitive packets, such as a packet reporting the movement of the opposing forces. Such selective dropping is hard to detect. In this paper, we propose a lightweight security scheme for detecting selective forwarding attacks. The detection scheme uses a multi-hop acknowledgement technique to launch alarms by obtaining responses from intermediate nodes. This scheme is efficient and reliable in the sense that an intermediate node reports any abnormal packet loss and suspect nodes to both the base station and the source node. To the best of our knowledge, this is the first paper that presents a detailed scheme for detecting selective forwarding attacks in the environment of sensor networks. The simulation results show that even when the channel error rate is 15%, simulating very harsh radio conditions, the detection accuracy of the proposed scheme is over 95%.

With more embedded systems networked, it becomes an important problem to effectively defend embedded systems against buffer overflow attacks. Due to the increasing complexity and strict requirements, off-the-shelf software components are widely used in embedded systems, especially for military and other critical applications. Therefore, in addition to effective protection, we also need to provide an approach for system integrators to efficiently check whether software components have been protected. In this paper, we propose the HSDefender (Hardware/Software Defender) technique to perform protection and checking together. Our basic idea is to design secure call instructions so systems can be secured and checking can be easily performed. In the paper, we classify buffer overflow attacks into two categories and provide two corresponding defending strategies. We analyze the HSDefender technique with respect to hardware cost, security, and performance. We experiment with our HSDefender technique on the simplescalar/ARM simulator with benchmarks from MiBench, an embedded benchmark suite. The results show that our HSDefender technique can defend a system against more types of buffer overflow attacks with less overhead compared with the previous work.

In embedded systems, high-performance DSP needs to be performed not only with high-data throughput but also with low-power consumption. This article develops an instruction-level loop-scheduling technique to reduce both execution time and bus-switching activities for applications with loops on VLIW architectures. We propose an algorithm, SAMLS (Switching-Activity Minimization Loop Scheduling), to minimize both schedule length and switching activities for applications with loops. In the algorithm, we obtain the best schedule from the ones that are generated from an initial schedule by repeatedly rescheduling the nodes with schedule length and switching activities minimization based on rotation scheduling and bipartite matching. The experimental results show that our algorithm can reduce both schedule length and bus-switching activities. Compared with the work of Lee et al. [2003], SAMLS shows an average 11.5% reduction in schedule length and an average 19.4% reduction in bus-switching activities.

This paper addresses high level synthesis for realtime digital signal processing (DSP) architectures using heterogeneous functional units (FUs). For such special purpose architecture synthesis, an important problem is how to assign a proper FU type to each operation of a DSP application and generate a schedule in such a way that all requirements can be met and the total cost can be minimized. In the paper, we propose a two-phase approach to solve this problem. In the first phase, we propose an algorithm to assign proper FU types to applications such that the total cost can be minimized while the timing constraint is satisfied. In the second phase, based on the assignments obtained in the first phase, we propose a minimum resource scheduling algorithm to generate a schedule and a feasible configuration that uses as little resource as possible. The experimental results show that our approach can generate high-performance assignments and schedules with great reduction on total cost compared with the previous work.

Previous approaches for the shortest path tree (SPT) dynamic update have mainly focused on the case of one link state change. Little work has been done on the problem of deriving a new SPT based on its old one for multiple link state decrements in a network that applies link-state routing protocols. The complexity of this problem comes from there being no accurate boundary of nodes to be updated in an updating process and that multiple decrements can be accumulated. Two dynamic algorithms (MaxR, MinD) are proposed to reduce the times for node updating. Compared with other algorithms for the SPT update of multiple edge weight decrements, our algorithms yield fewer times for node updates during the dynamic update process. Such an achievement is attained by the mechanism of part node updating in a branch on the SPT after a particular node selection from a built node list. Simulation results are given to show our improvements.

Software pipelining technique is extensively used to exploit instruction-level parallelism of loops, but also significantly expands the code size. For embedded systems with very limited on-chip memory resources, code size becomes one of the most important optimization concerns. This paper presents the theoretical foundation of code size reduction for software-pipelined loops based on retiming concept. We propose a general Code-size REDuction technique (CRED) for various kinds of processors. Our CRED algorithms integrate the code size reduction with software pipelining. The experimental results show the effectiveness of the CRED technique on both code size reduction and code size/performance trade-off space exploration.

Shortest path tree (SPT) computation is the major over-head for routers using any link-state routing protocols including the most widely used OSPF and IS-IS. Changes of link states are nowadays commonly occurred. It is not efficient and stable for network routing to use traditional static SPT algorithms to recompute the whole SPT whenever a change happens. We present new dynamic algorithms to compute and update the SPT with the minimum computational overhead. Routing stability is achieved by having the minimum changes in the topology of an existing SPT when some link states are changed. To the authors' knowledge, our algorithms outperform the best existing ones in the literature.