Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This paper introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information. LOECM is formulated as an online stochastic optimization problem and leverages Lyapunov optimization techniques to balance immediate cost minimization against task queue stability. By exploiting the inherent structure of the optimization problem, we design an efficient solver with constant time and space complexity ($\mathcal{O}(1)$). Additionally, we propose the LOECM scheduling policy to dynamically manage workloads and node activation based on a covering subset strategy. To evaluate the practicality and effectiveness of our approach, we implement a prototype system called LOECMHadoop, integrating the LOECM scheduler into Hadoop-a widely-used data processing platform-across a 10- node cluster. Experimental results show that our power model has an average deviation of approximately 5%, and LOECM achieves over 20% electricity cost savings compared without our policy on the prototype system.

Federated Learning (FL) exhibits susceptible to model poisoning attacks, which compromise the availability of the collaboratively trained model by introducing detrimental local updates during the training process. The predominant line of defense against such attacks has been to impose stringent restrictions on clients' model updates. However, this strategy raises new vulnerabilities where the global model can be infiltrated by meticulously crafted malicious perturbations. This vulnerability arises due to the model's inherent sensitivity to perturbations, making it exposed and fragile. In response, this work investigates a novel defensive paradigm centered on model stability-specifically, a model's resilience against perturbations within its parameter space. As a solution, we introduce a new method named Model Stability Defense for Federated Learning (MSDFL), designed to fortify the defense of FL systems against model poisoning attacks. MSDFL utilizes a minmax optimization framework, which is fundamentally linked to empirical risk for exploring the effects of model perturbations. The core aim of our approach is to minimize the norm of the model-output Jacobian matrix without compromising predictive performance, thereby establishing defense through enhanced model stability. Moreover, we propose a refined version of MSDFL, named Holistic Model Stability Defense for Federated Learning (HMSDFL), which considers model stability across all output dimensions of the logits to effectively eradicate the disparity in model convergence speed induced by MSDFL. Extensive experimental results fully demonstrate the fidelity, robustness, compatibility, and self-protection of our methods. The source codes are maintained athttps://github.com/qqoneone/MSDFL.

Task matching is a core component of crowdsourcing systems, enabling efficient assignment of tasks to appropriate workers. However, this process often requires both task requesters and workers to disclose sensitive contextual information, such as spatial locations and temporal availability, raising serious privacy concerns. Although various cryptographic techniques and blockchain-based solutions have been proposed to preserve privacy and enhance trust, existing schemes still face three critical limitations: reliance on centralized key authorities, lack of support for fine-grained spatio-temporal constraints, and inefficient public verification mechanisms. To address these challenges, we propose PVSTMatch, a blockchain-empowered privacy-preserving spatio-temporal task matching scheme in crowdsourcing. PVSTMatch eliminates the need for trusted third parties through an authority-free authorization mechanism, supports secure spatio-temporal matching by designing a compact secure comparison method, and enables efficient public verifiability via a succinct verification mechanism. Furthermore, we introduce GE-PVSTMatch, a gas-efficient variant that significantly reduces on-chain storage costs. Security analysis and performance evaluation demonstrate that our schemes achieve strong bilateral privacy, verification correctness, and practical efficiency, making them well-suited for real-world decentralized crowdsourcing platforms.

Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory requirements. However, existing FU methods often struggle to balance effective erasure with model utility preservation, especially for class-level unlearning in non-IID settings. We propose Federated Unlearning via Class-aware Representation Transformation (FUCRT), a novel method that achieves unlearning through class-aware representation transformation. FUCRT employs two key components: (1) a transformation class selection strategy to identify optimal forgetting directions, and (2) a transformation alignment technique using dual class-aware contrastive learning to ensure consistent transformations across clients. Extensive experiments on four datasets demonstrate FUCRT's superior performance in terms of erasure guarantee, model utility preservation, and efficiency. FUCRT achieves complete (100\%) erasure of unlearning classes while maintaining or improving performance on remaining classes, outperforming state-of-the-art baselines across both IID and Non-IID settings. Analysis of the representation space reveals FUCRT's ability to effectively merge unlearning class representations with the transformation class from remaining classes, closely mimicking the model retrained from scratch.

Content based video search services find extensive applications across various domains including video surveillance and object detection. In recent times, researchers have increasingly turned their attention towards enhancing the security of video search over outsourced encrypted videos. Nonetheless, prior researchers often leverage cost-expensive techniques like Homomorphic encryption or Order-preserving encryption to ensure privacy preservation. To reduce the overhead, Bloom Filter (BF)-encoded keyword search is a promising technology for retrieving encrypted videos with image queries. However, it generally suffers from serious data privacy leakage since it will reveal the inclusion relationship between “1” and “0” in the BF. Fortunately, the privacy-preserving bloom filter-based search scheme (PBKS) was recently proposed to achieve secure and effective search while protecting the values in BFs, but it still has two limitations. One is the size of a search token is very large in some cases and the other is the cloud server can infer the true value of each bit in the BF by doing a few operations. In this paper, we propose an efficient and confidentiality-preserving bloom filter-encoded video search (ECVS) scheme for retrieving encrypted videos with image queries. We first design a new CPRF (prefix-constrained pseudorandom function)-based token compression method to reduce the size of the search token and reduce the communication cost largely. Furthermore, we customize a periodic refresh mechanism to conceal the true value of each bit in the BF while avoiding excessive computational pressure on resource-limited users. Security analysis and experiments confirm the security and efficiency of our schemes.

Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned blockchain poses potential data privacy risks and substantial storage costs, hindering its usage as a storage medium. These issues necessitate the usage of secure data deduplication technology to mitigate them. Unfortunately, current secure data deduplication schemes are predominantly designed with centralized cloud servers in mind and are not suitable for distributed blockchain systems. The reason is that the full backup feature of the permissioned blockchain renders a wide attack surface to offline brute-force and frequency analysis attacks. In response, we propose DedupChain, a secure blockchain-enabled storage system with deduplication for zero-trust networks. DedupChain employs a trusted execution environment (i.e., Inter SGX enclave) in conjunction with Oblivious RAM (ORAM) to offer a novel security guarantee namedoblivious data deduplication, which empowers DedupChain with the ability to defend offline brute-force and frequency analysis attacks. DedupChain also proposes several novel techniques to address the security and efficiency issues raised by the SGX enclave. We implemented a system prototype of DedupChain and evaluated its performance metrics. Our experimental results show that DedupChain exhibits satisfactory operational delays, throughput, and storage overhead. Security analysis shows that DedupChain is robust enough to withstand several types of attacks. To the best of our knowledge, we are the first to apply secure data deduplication techniques to address data privacy and storage cost issues raised by permissioned blockchain when used as a zero-trust storage medium.

Paging and exit overheads have been proven to be the performance bottlenecks when adopting Searchable Symmetric Encryption (SSE) with trusted hardware such as Intel SGX for keyword search. This problem becomes more serious when incorporating ORAM and SGX to design oblivious SSE schemes such as POSUP [1] and Oblidb [2] which can defend against inference attacks. The main reason comes from high round communication complexity of ORAM and constrained trusted memory created by SGX. To overcome this performance bottleneck, we propose a set of novel SSE constructions with realistic security/performance trade-offs. Our core idea is to encode the keyword-identifier pairs into a bloom filter to reduce the number of ORAM operations during the search procedure. Specifically, Construction 1 loads the bloom filter into the enclave sequentially, which outperforms about$1.7\times$when the dataset is large compared with the performance of the baseline that directly combines ORAM and SGX. To further improve the performance of Construction 1, Construction 2 classifies keywords into groups and stores these groups in different bloom filters. By additionally leaking the keywords in search token belonging to which groups, Construction 2 outperforms Construction 1 by$16.5\sim 36.8\times$and provides an improvement of at least one order over state-of-the-art oblivious protocols.

Dynamic searchable symmetric encryption (DSSE) enables clients to store encrypted data on untrusted servers while retaining the ability to search and update the data efficiently. However, most existing DSSE schemes are vulnerable to incorrect update queries, such as duplicated insertions or invalid deletions, which can compromise both security and availability. Although existing robust schemes have made progress in addressing these issues, they still suffer from significant search inefficiencies, particularly when handling large numbers of updates. To overcome these limitations, we proposeRO(SE)2, a novel robust DSSE scheme that simultaneously achieves robustness, forward-and-Type-III-backward security, and optimal search performance.RO(SE)2introduces a hierarchical binary tree structure combined with an oblivious map (OMAP) to handle incorrect updates during the update phase, eliminating the need for filtering during search queries and significantly improving search efficiency. Additionally,RO(SE)2employs a two-layer encryption mechanism to ensure forward security and supports efficient search result verification through its verifiable extension,RO(SE)2-v. Rigorous security analysis proves thatRO(SE)2can achieve not only robustness, forward and backward security but optimal search efficiency as well. Comparative analysis reveals thatRO(SE)2outperforms existing robust schemes in terms of search performance, whileRO(SE)2-v outperforms the state-of-the-art verifiable robust schemes in verification performance.

Federated Learning (FL) is vulnerable to model poisoning attacks, where malicious updates (e.g., gradients) can adversely interfere with the global model. Existing attacks typically rely heavily on the updates of benign clients and aggregation algorithms to craft malicious updates. However, the benign updates and aggregation algorithms are usually hard to access for attackers, which makes their attacks weak and volatile. Therefore, in this work, we aim to design an adaptive model poisoning attack based on the agnostic adversary. Specifically, we propose a new concept from the perspective of adversarial learning, called adversarial model perturbation. This perturbation targets the parameters of the local model and aims to maximally mislead its predictions. Then, we develop a novel adaptive model poisoning attack namedAdversarial Model Attack (AMA), which utilizes the adversarial model perturbation as the malicious updates to attack the global model. Instead of the benign updates and aggregation algorithms, we only leverage the original data of the malicious client to adaptively craft the malicious updates. AMA resolves the conflict between the knowledge requirement of the adversary and the impact of model poisoning attacks. Empirical results against multiple robust FL methods show that AMA surpasses state-of-the-art attack methods and updates the benchmark of attack impact on Fedavg, Trimean, Multi-Krum, FoundationFL, RFA, and Median.

Federated semi-supervised learning (FSSL), facilitates labeled clients and unlabeled clients jointly training a global model without sharing private data. Existing FSSL methods predominantly employ pseudo-labeling and consistency regularization to exploit the knowledge of unlabeled data, achieving notable success in raw data utilization. However, the effectiveness of these methods is challenged by large deviations between uploaded local models of labeled and unlabeled clients, as well as confirmation bias introduced by noisy pseudo-labels, both of which negatively affect the global model's performance. In this paper, we present a novel FSSL method called Dual Class-aware Contrastive Federated Semi-Supervised Learning (DCCFSSL). This method considers both the local class-aware distribution of each client's data and the global class-aware distribution of all clients’ data within the feature space. By implementing a dual class-aware contrastive module, DCCFSSL establishes a unified training objective for different clients to tackle large deviations and incorporates contrastive information in the feature space to mitigate confirmation bias. Additionally, DCCFSSL introduces an authentication-reweighted aggregation technique to improve the server's aggregation robustness. Our comprehensive experiments show that DCCFSSL outperforms current state-of-the-art methods on three benchmark datasets and surpasses the FedAvg with relabeled unlabeled clients on CIFAR-10, CIFAR-100, and STL-10 datasets.

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.

Data deduplication is of vital importance for mobile cloud computing to cope with the explosive growth of outsourced mobile data. In order to ensure the privacy of sensitive mobile data against an untrusted cloud, Message-Locked Encryption (MLE) has been proposed to enable deduplication over ciphertext. However, MLE prohibits data access control since it uses deterministic content-derived encryption keys. Recently, a lightweight rekeying-aware encrypted deduplication system (REED) has been proposed to achieve dynamic access control for secure data deduplication. However, REED is vulnerable to key-retaining attack and stub-retaining attack, which leads to insecure access revocation, and thus cannot support secure dynamic access control. In response, we present AC-Dedup, an encrypted deduplication storage system that supportssecure dynamic access controlfor mobile cloud storage. At the core of AC-Dedup are two novel encryption techniques namedmixed message locked encryptionandrandom stub re-encryptionto resist the two types of attacks, respectively. To the best of our knowledge, AC-Dedup is the first practical system that achieves secure data deduplication and secure dynamic access control simultaneously. We conduct security analysis and experimental evaluation on mobile device and cloud platform with real-world IoT datasets. The results show that AC-Dedup enables secure and efficient dynamic access control while preserving deduplication effectiveness.

Blockchain databases have been exploited in many applications to construct trust and share data among multiple participants. However, maintaining the entire blockchain locally will cause heavy communication and storage overhead for users with limited resources. Alternatively, the user could act as a light node that stores block headers only and delegates queries to full nodes that maintain the entire blockchain. However, introducing a light node raises several concerns about query integrity and privacy. In this paper, we propose EPPVChain, the first scheme that simultaneously achieves efficient, privacy-preserving and verifiable conjunctive query for blockchain databases. EPPVChain resorts to a novel symmetric cryptographic primitive named Symmetric Hidden Vector Encryption (SHVE), and deploys several new techniques to achieve the desired goals. In specific, we design a new SHVE-based authenticated data structure to support privacy-preserving verifiable conjunctive queries. We further propose two improved schemes to aggregate data records to optimize query performance. Finally, we propose a dual-chain key escrow protocol to securely escrow the symmetric key of SHVE without relying on any trusted third party. The security analysis and evaluation confirm EPPVChain’s ability to achieve query privacy and integrity with high efficiency.

As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to boost the realization of intelligent transportation system. Recently, vehicular data privacy has been paid increasing attention in data sharing. Searchable encryption as a promising cryptographic primitive can be utilized to ensure vehicular data confidentiality without sacrificing data searchability. However, most vehicular data sharing schemes rely on centralized cloud servers, which are vulnerable to the single point of failure and distributed denial of service (DDoS) attacks. In this paper, we propose VehShare, a decentralized framework for privacy-preserving vehicular data sharing. We resort to the smart contract to implement a trusted platform for vehicles to share their encrypted vehicular data. To provide efficient access control, we design an authorization-based on-chain access control scheme with a lightweight cryptographic primitive. Moreover, we design a time synchronization-based non-interactive search token generation scheme to achieve efficient privacy-preserving search queries, while satisfying forward and backward security. We formally analyze the security of VehShare and extensive experiments demonstrate the efficiency of VehShare.

The rapid development of Internet of Things (IoT) enables digital twin (DT) technology to precisely represent a real product in a virtual space by generating a multitude of IoT data items to record many aspects of the product. To support various DT-based applications, the generated IoT data items need to be shared among multiple parties involving the lifecycle of the product, which raises increasing demand for data access control. The decentralization and tamper-proofing properties of blockchain enable it a promising technology to support immutability protection of shared IoT data items. Meanwhile, to protect the confidentiality of the shared IoT data items, attribute-based encryption (ABE) can be used as a common tool to construct a cryptographic enforced data access control scheme. However, its adoption has been severely hindered by the incompatibility between the immutability of blockchain and secure authority update of cryptographic enforced data access control. In this paper, a blockchain-aware rollbackable data access control scheme (Bdacs) is proposed to reconcile the above tension. Bdacs uses two novel encryption schemes named hierarchical encryption scheme and privacy-preserving rollback re-encryption scheme to realize secure dynamic access control while preserving the immutability of blockchain. We prove the security of Bdacs and evaluate it through theoretical comparison and experimental analysis to confirm its efficiency. This work can serve as a basis of development of future DT-based applications to enable privacy-preserving IoT data-sharing systems deployed on blockchain.

Since 2011, the private ride-hailing companies Didi (2019), Uber (2019) and Lyft (2021) have expanded into more and more cities. These ride-hailing services (RHS) bring convenience to our life; however, at the same time they, also raise security concerns for users. For example, several recent news items show that a considerable number of registered drivers whose licenses have been revoked are still taking RHS orders on the respective platforms; this phenomenon directly leads to insecurity on part of its users and the bad reputation of the ride-hailing service provider (SP). The traditional solution to solve this problem is to periodically check the validity of the drivers’ licenses; however, it is a considerably time-consuming and costly process since the SPs have to manually interact with the governing authorities. Therefore, in this paper, we have presented an auditable self-sovereign identity system (named AudiSSI), which provides an efficient approach for the SPs to manage their registered drivers’ qualifications in a decentralized and automatic manner. Further, using smart contract technology, we propose a safety guarantee insurance in the form of an auditing contract to enable the RHS rider to check their driver's qualifications before the trip starts and get incentives once they detect a disqualified driver. We designed an incentive mechanism and have provided a game theoretical analysis. Finally, we implemented a prototype of AudiSSI and deployed it on Hyperledger Indy and Fabric to show that self-sovereign identity system for RHS driver with qualification auditing is efficient and technically feasible.

In this article, we propose Double Edged (DE)-Sword, an incentivized verifiable tag path query scheme. DE-Sword queries tag records stored across a path of participants within an RFID-enabled supply chain in a verifiable way. Different with previous works, DE-Sword works in a dishonest-data owner model in which participants are the owners of tag records and may be dishonest. DE-Sword introduces a novel double-edged reputation incentive mechanism to encourage participants to behave honestly; and couples it with cryptographic primitives to ensure query verifiability. We evaluate DE-Sword through game theory, security analysis, and performance evaluation. The game theory and security analysis shows that DE-Sword guarantees query verifiability. The evaluation results show that DE-Sword incurs low overhead in supply chain systems.

Deploying data storage and query service in an untrusted cloud server raises critical privacy and security concerns. This paper focuses on the fundamental problem of processing conjunctive keyword queries over an untrusted cloud in a privacy-preserving manner. Previous tree-based searchable symmetric encryption (SSE) schemes, such asIBTreeandVBTree, can process conjunctive keyword queries in a secure and efficient way. However, these schemes cannot address “Result Pattern (RP)” leakage, which can be used to recover the keywords contained in a conjunctive keyword query. To combat this challenging problem, we propose a result pattern hiding conjunctive query scheme namedRphxusing Intel SGX. In particular, we first propose a new “SGX-aware” compressed index namedVIBTby combining variable-length bloom filter tree, matryoshka filter and online cipher. To achieveRPhiding, we then introduce a new tree-based SSE scheme namedRphxby deployingVIBTto Intel SGX. Security analysis shows thatRphxcan enhance the security requirements by hidingRPleakage under the IND-CKA2 security model. Experimental results show thatVIBTgains at least$30\times $improvement in storage efficiency andRphxcan achieve comparable search efficiency comparing with previous works.

The application of blockchain to Vehicular Edge Computing (VEC) has attracted significant interests. As the Internet of Things plays an essential and fundamental role for data collecting, data analyzing, and data management in VEC, it is vital to guarantee the security of the data. However, the resource-constraint nature of edge node makes it challenging to meet the needs to maintain long life-cycle IoT data since vast volumes of IoT data quickly increase. In this paper, we propose Acce-chain, a storage-elastic blockchain based on different storage capacities at the edge. Acce-chain supports re-write operation to re-write the historical block with a newly generated block without breaking the hash links between the blocks. As a result, Acce-chain ensures that the hot data can be efficiently accessed at the edge without incurring much communication costs or increasing the total size of the chain. To guarantee the security of the re-write process, we propose a new cryptographic primitive named Dynamic Threshold Trapdoor Chameleon Hash (DTTCH). To guarantee the verifiability of query operation, we design a novel storage structure namedHybridStoreto ensure the verifiable query for on-chain/off-chain IoT data. As a result, Acce-chain achieves both authorized re-write and verifiable query simultaneously. We provide security analysis for the DTTCH scheme and the IoT data query algorithms. We evaluate Acce-chain through experiments and the results show that the performance of the re-write operation is feasible in real-world VEC settings, and the query efficiency can achieve up to several magnitudes better than which of the baseline. The results also demonstrate that Acce-chain can provide high service quality for the latency-sensitive VEC systems.

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users’ personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchaseFair Price Insurancein the form ofPrice Auditing Contract, then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.

Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate traceability of items by providing inconsistent item data. To fix this issue, we propose Acics, an item data consistency auditing scheme in untrusted cloud services for cloud-aided IIoT systems. Acics presents two variants named S-Acics and L-Acics. S-Acics enables industrial participants to audit item data consistency for each item and circularly play the auditing role. L-Acics further enables industrial participants to audit item data consistency for a sampled subset of items while resisting data selection attack via a new separated storage mechanism. Finally, Acics integrates a fair payment mechanism built on smart contract to incentivize the cloud server to provide consistent item data access service for industrial participants. The experiment results show that our solution can audit item data consistency with reasonable cost.

Convolutional neural networks (CNNs) have recently made great progress in single image super-resolution (SISR) due to their powerful feature representation. However, most existing CNN-based SR methods mainly focus on designing deeper architectures to obtain superior representations, neglecting the global feature correlations inside networks, thus limiting the discriminative learning ability. In this paper, we propose a spatial graph convolutional network (SGCN) to amplify feature representation for high quality image rendering. Specifically, we present a residual feature refinement module (RFRM) in SGCN to encode correlations in both channel and spatial dimensions of features. A novel spatial graph attention (SGA) is deployed to model the correlations with awareness of global information for the feature enhancement. Further-more, we utilize the Gram matrix to infer the global relations in features, which adaptively measures the pixel-wise spatial attention with free parameters. Experimental results demonstrate the superiority of our SGCN over state-of-the-art SISR methods in terms of quantitative metrics and visual quality.

Adversarial Examples threaten to fool deep learning models to output erroneous predictions with high confidence. Optimization-based methods for constructing such samples have been extensively studied. While being effective in terms of aggression, they typically lack clear interpretation and constraint about their underlying generation process, which thus hinders us from leveraging the produced adversarial samples for model protection in the reverse direction. Hence, we expect them to repair bugs in the pre-trained models by produced additional training data equipped with strong attack ability rather than time-consuming full re-training from scratch. To address these issues, we first study the black-box behaviors and the intrinsic deficiency of neighborhood information in previous optimization-based adversarial attacks and defenses, respectively. Then we introduce a new method dubbed FeaCP, which uses correct predicted samples in disjoint classes to guide the generation of more explainable adversarial samples in the ambiguous region around the decision boundary instead of uncontrolled “blind spots”, via convex combination in a feature component-wise manner which takes the individual importance of feature ingredients into account. Our method incorporates the prior fact that for well-separated samples, the path connecting them would go through model's decision-boundary that lies in a low-density region, however, wherein adversarial examples are spread with high probability, thus having an impact on the ultimate trained model. In our work, the path is constructed by proposed inhomogeneous feature-wise convex interpolation rather than operating on sample-wise level, limiting the search space of FeaCP to obtain an adaptive neighborhood. Finally, we provide detailed insights and extend our method to adversarial fine-tuning using vicinity distribution to optimize the approximated decision boundary, and validate the significance of our FeaCP to model performance. The experimental results show that our method provides competitive performance on various datasets and networks.

Intel Software Guard extensions (SGX), a hardware-based Trusted Execution Environment (TEE), has become a promising solution to stopping critical threats such as insider attacks and remote exploits. SGX has recently drawn extensive research in two directions---using it to protect the confidentiality and integrity of sensitive data, and protecting itself from attacks. Both the applications and defense mechanisms of SGX have a fundamental need---flexible memory protection that updates memory-page permissions dynamically and enforces the least-privilege principle. Unfortunately, SGX does not provide such a memory-protection mechanism due to the lack of hardware support and the untrustedness of operating systems.

Cloud service is an essential technique to provide product traceability for industrial systems by providing data integration and sharing services. However, a malicious or corrupted cloud service may prevent industrial participants from acquiring accurate and consistent traceability of products. To fix this issue, we propose Acics, a fast consistency auditing scheme for massive industrial data in untrusted cloud services. Our scheme enables industrial participants to circularly play the role of the auditor to audit data consistency of products in real-time. Additionally, we design a separated storage mechanism to improve the auditing efficiency for massive industrial data by utilizing and tailoring ORAM. The evaluation indicates that our solution audits data consistency with reasonable cost.

Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. Although being successful, the connection between the synthesized data and the original data is largely ignored in training, without considering the distribution information that the synthesized samples are surrounding the original sample in training. Hence, the behavior of the network is not optimized for this. However, that behavior is crucially important for generalization, even in the adversarial setting, for the safety of the deep learning system. In this work, we propose a framework called Stochastic Batch Augmentation (SBA) to address these problems. SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples. The proposed regularization provides direct supervision by the KL-Divergence between the output soft-max distributions of original and virtual data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet show that SBA can improve the generalization of the neural networks and speed up the convergence of network training.

Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency.

The OS kernel is critical to the security of a computer system. Many systems have been proposed to improve its security. A fundamental weakness of those systems is that page tables, the data structures that control the memory protection, are not isolated from the vulnerable kernel, and thus subject to tampering. To address that, researchers have relied on virtualization for reliable kernel memory protection. Unfortunately, such memory protection requires to monitor every update to the guest's page tables. This fundamentally conflicts with the recent advances in the hardware virtualization support. In this paper, we present the design and implementation of SecPod, a practical and extensible framework for virtualization-based security systems that can provide both strong isolation and the compatibility with modern hardware. SecPod has two key techniques:paging delegationdelegates and audits the kernel's paging operations to a secure space;execution trappingintercepts the (compromised) kernel's attempts to subvert SecPod by misusing privileged instructions. We have implemented a prototype of SecPod based on KVM. Our experiments show that SecPod is both effective and efficient.

Modern computing systems especially cloud-based and cloud-centric systems always consist of a mass of components running in large distributed environments with complicated interactions. They are vulnerable to performance problems due to the highly dynamic runtime environment changes (e.g., overload and resource contention) or software bugs (e.g., memory leak). Unfortunately, it is notoriously difficult to diagnose the root causes of these performance problems in a fine granularity due to complicated interactions and a large cardinality of potential cause set. In this paper, we build an automated, black-box and end-to-end cause inference system named CauseInfer to pinpoint the root causes or at least provide some hints. CauseInfer can automatically map a distributed system to a two-layer hierarchical causality graph and infer the root causes along the causal paths in the causality graph. CauseInfer models the fault propagation paths in an explicit way and works without instrumentation to the running production system, which makes CauseInfer more effective and practical than previous approaches. The experimental evaluations in two benchmark systems show that CauseInfer can identify the root causes in a high accuracy. Compared to several state-of-the-art approaches, CauseInfer can achieve over 10 percent improvement. Moreover, CauseInfer is lightweight and flexible enough to readily scale out in large distributed systems. With CauseInfer, the mean time to recovery (MTTR) of the cloud systems can be significantly reduced.

Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites.To improve the programming efficiency, we present a Java API suggestion approach called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context.Previous related works use statistical methods based on Hidden Markov Model (HMM), which require establishing one specific model for each class.We propose Determininstic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM.To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with top-K accuracy.Results show that APIHelper outperforms other prior works both on accuracy and computation efficiency.

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N -gram-based malware detection which is similar to our method.

Malware detection plays a crucial role in computer security. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset.

Even well-designed software systems suffer from chronic performance degradation, also known as “software aging”, due to internal (e.g., software bugs) or external (e.g., resource exhaustion) impairments. These chronic problems often fly under the radar of software monitoring systems before causing severe impacts (e.g., system failures). Therefore, it is a challenging issue how to timely predict the occurrence of failures caused by these problems. Unfortunately, the effectiveness of prior approaches are far from satisfactory due to the insufficiency of aging indicators adopted by them. To accurately predict failures caused by software aging which are named as Aging-Related Failure (ARFs), this paper presents a novel entropy-based aging indicator, namely Multidimensional Multi-scale Entropy (MMSE) which leverages the complexity embedded in runtime performance metrics to indicate software aging. To the best of our knowledge, this is the first time to leverage entropy to predict ARFs. Based upon MMSE, we implement three failure prediction approaches encapsulated in a proof-of-concept prototype named ARF-Predictor. The experimental evaluations in a Video on Demand (VoD) system, and in a real-world production system, AntVision, show that ARF-Predictor can predict ARFs with a very high accuracy and a low Ahead-Time-To-Failure (ATTF). Compared to previous approaches, ARF-Predictor improves the prediction accuracy by about 5 times and reduces ATTF even by 3 orders of magnitude. In addition, ARF-Predictor is light-weight enough to satisfy the real-time requirement.

Time series prediction problems can play an important role in many areas, and multi-step ahead time series forecast, like river flow forecast, stock price forecast, could help people to make right decisions. Many predictive models do not work very well in multi-step ahead predictions. LSTM (Long Short-Term Memory) is an iterative structure in the hidden layer of the recurrent neural network which could capture the long-term dependency in time series. In this paper, we try to model different types of data patterns, use LSTM RNN for multi-step ahead prediction, and compare the prediction result with other traditional models.

In recent years, with the large-scale growth of the high-energy physics experimental data, the performance of metadata retrieval based on disk storage has been gradually reduced, which can not meet the retrieval performance requirements of EB-level high-energy physics experimental metadata. To solve this problem, a method of converting traditional directory structure storage into RAMCloud storage is proposed. The core idea of this method is to use Key-Value non-relational database to re-design the traditional directory tree, separate directory structure and directory node content, and add a secondary index for parent directory, which can give full play to Key-Value retrieval and memory storage advantages, improve search efficiency. Through the implementation of the test, showed that the method has a better performance. Compared to the storage based on Mysql, the retrieval time drops significantly in the case of increased data.

Software memory disclosure attacks, such as buffer over-read, often work quietly and would cause secret data leakage. The well-known OpenSSL Heartbleed vulnerability leaked out millions of servers' private keys, which caused most of the Internet services insecure at that time. Existing solutions are either hard to apply to large code bases (e.g., through formal verification [20] or symbolic execution [8] on program code), or too heavyweight (e.g., by involving a hypervisor software [23], [24] or a modified operating system kernel [17]). In this paper, we propose SecretSafe, a lightweight and easy-to-use system which leverages the traditional x86 segmentation mechanism to isolate the application secrets from the remaining data. Software developers could prevent the secrets from being leaked out by simply declaring the secret variables with SECURE keyword. Our customized compiler will automatically separate the secrets from the remaining non-secret data with an isolated memory segment. Any legal instructions that have to access the secrets will be automatically instrumented to enable accesses to the isolated segment. We have implemented a SecretSafe prototype with the open source LLVM compiler framework. The evaluation shows that SecretSafe is both secure and efficient.

Querying the path information of individual products in a supply chain is key to many applications. RFID (Radio-Frequency IDentification) is a main technology to enable product path information query today. With RFID technology, supply chain participants can efficiently track products in transit and record their production information in databases. In this paper, we investigate the following question: how can we conduct privacy-preserving product path information query with verifiability on an RFID-enabled distributedsupplychain?WeaddressthisquestionwithDouble Edged(DE)-Sword,anincentivizedverifiablequerysystem. DESword introduces a novel double-edged reputation incentive mechanism to encourage supply chain participants to behave; and couples it with cryptographic primitives and careful protocol design. We evaluate DE-Sword through security analysis and performance experiments. The security analysis shows that DE-Sword guarantees both verifiability and privacy. The experiment results show that DE-Sword achieves low overhead in RFID-enabled supply chain applications.

Remote server vulnerability exploit is one of the most troublesome threat to the Internet security. An effective defense against the remote vulnerability exploit is code randomization, which randomizes the program code address to disrupt the malicious payload execution. Unfortunately, code randomization is particularly susceptible to address exposure vulnerabilities; the leak of a single code or data pointer is often sufficient to de-randomize the protected process. Existing solutions either prevent part of the address exposures (e.g., code-pointer exposure only), or are too heavyweight (e.g., have to involve a hypervisor software or a modified OS kernel). In this paper, we propose AXIS that can provide existing code randomization techniques with a comprehensive protection against address exposure. AXIS first redirects the code pointers through an indirection table that is protected by the execute-no-read memory segment. During the load time, all static data will be relocated to random locations, which breaks the fixed offsets between code and data. We have implemented a prototype of AXIS with only a customized compiler and a pre-loaded library. Our experiments show that AXIS can successfully eliminate address exposure with a minimal performance overhead.

In order to simultaneously power and cool hundreds of thousands of servers, large-scale data centers usually consume several to tens of megawatts of electricity. This enormous electricity consumption leads to considerable concerns in the electricity cost including both electricity bills and carbon tax. To achieve a sustainable data center, many Internet service providers begin to build their own on-site renewable energy plants to help reduce the electricity cost. However, considering the performance constraint of delay tolerant workloads and the lack of future information about the time-varying electricity price, carbon emission rate, and available on-site renewable energy, it is a fairly challenging problem that how to schedule the delay tolerant workloads to reduce the electricity cost of a sustainable data center. To address this challenging optimization problem, this paper proposes an online workload scheduling algorithm CECM based on the Lyapunov optimization framework, which is able to tradeoff between the electricity cost and the performance of delay tolerant workloads without any future information about the time-varying system states. With extensive simulations based on the real-life traces, we show that CECM is able to reduce the electricity cost by 9.26 percent, while still guaranteeing the performance constraint of delay tolerant workloads.

Cloud computing promises high performance and cost-efficiency, however, most cloud infrastructures operate at low utilization which greatly adhere cost effectiveness. Previous works focus on seeking efficient virtual machine (VM) consolidation strategies to increase the utilization of virtual resources in production environment, while overlooking the under-utilization of backup virtual resources. We propose a heuristic time sharing policy derived from the restless multi-armed bandit problem. The proposed policy achieves increasing backup virtual resources utilization while providing high availability. The experiment results show that the traditional 1:1 backup provision can be extended to 1:M (M>>1) between the backup VM and the service VMs, and the utilization of backup VMs can be enhanced significantly.

Malicious OS kernel can easily access user's private data in main memory and pries human-machine interaction data, even one that employs privacy enforcement based on application level or OS level. This paper introduces AppSec, a hypervisor-based safe execution environment, to protect both the memory data and human-machine interaction data of security sensitive applications from the untrusted OS transparently.

As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, calledPowerTracer, for diagnosing energy inefficiency and saving power. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy inefficiency diagnosis and power saving in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop (1) a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency, and (2) a power saving method that enables dynamic voltage and frequency scaling (DVFS) with online request tracing. We implement a prototype of PowerTracer, and conduct extensive experiments to validate its effectiveness. Our tool analyzes several state-of-the-practice and state-of-the-art DVFS control policies and uncovers existing energy inefficiencies. Meanwhile, the experimental results demonstrate that PowerTracer outperforms its peers in power saving.

Prior work on anti-collision for radio frequency identification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of tag collision, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6× improvement on the identification throughput in a large-scale dense reader environment.

Modern applications especially cloud-based or cloud-centric applications always have many components running in the large distributed environment with complex interactions. They are vulnerable to suffer from performance or availability problems due to the highly dynamic runtime environment such as resource hogs, configuration changes and software bugs. In order to make efficient software maintenance and provide some hints to software bugs, we build a system named CauseInfer, a low cost and blackbox cause inference system without instrumenting the application source code. CauseInfer can automatically construct a two layered hierarchical causality graph and infer the causes of performance problems along the causal paths in the graph with a series of statistical methods. According to the experimental evaluation in the controlled environment, we find out CauseInfer can achieve an average 80% precision and 85% recall in a list of top two causes to identify the root causes, higher than several state-of-the-art methods and a good scalability to scale up in the distributed systems.

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~ 10cm) can be precisely perceived, and the accuracy is up to 92.34% while the false positive is suppressed under 0.5%.

Software systems that run continuously over a long time have been frequently reported encountering gradual degradation issues. That is, as time progresses, software tends to exhibit degraded performance, deflated service capacity, or deteriorated QoS. Currently, the state-of-the-art approach of Mann-Kendall Test & Seasonal Kendall Test & Sen's Slope Estimator & Seasonal Sen's Slope Estimator (MKSK) detects and characterizes degradation via a combination of techniques in statistical trend analysis. Nevertheless, we pinpoint some drawbacks of MKSK in this paper: 1) MKSK cannot be automated for large scale software degradation analysis, 2) MKSK estimates the degradation trend of software in an oversimplified linear way, 3) MKSK is sensitive to noise, and 4) MKSK suffers from high computational complexity. To overcome all these limitations, we propose a more advanced approach called Modified Cox-Stuart Test & Iterative Hodrick-Prescott Filter (CSHP). The superiority of our CSHP approach over MKSK is validated through extensive Monte Carlo simulations, as well as a real performance dataset measured from 99 real-world web servers.

The era of big data has began. Although applications based on big data bring considerable benefit to IT industries, governments and social organizations, they bring more challenges to the management of big data platforms which are the fundamental infrastructures due to the complexity, variety, velocity and volume of big data. To offer a healthy platform for big data applications, we propose a novel signature-based performance diagnosis approach employing MIC invariants between performance metrics. We formalize the performance diagnosis as a pattern recognition problem. The normal state of a big data application is used to train a set of MIC (Maximum Information Criterion) invariants. One performance problem occurred in the big data application is identified by a unique binary tuple consisted by a set violations of MIC invariants. All the signatures of performance problems form a diagnosis knowledge database. If the KPI (Key Performance Indicator) of the big data application deviates its normal region, our approach can identify the real culprits through looking for similar signatures in the signature database. To detect the deviation of the KPI, we propose a new metric named unpredictability based on ARIMA model. And considering the variety of big data applications, we build an ensemble performance diagnosis approach which means a unique ARIMA model and a unique set of MIC invariants are built for a specific kind of application. Through experiment evaluation in a controlled environment running a state of the art big data benchmark, we find our approach can pinpoint the real culprits of performance problems in an average 83% precision and 87% recall which is better than a correlation based and single model based performance diagnosis.

The recent years witnessed rapid emergence and proliferation of cloud computing. To fully utilize the compute resources, some cloud operators provide spot resources. Spot resources allow customers to bid on unused capacity. However, pricing policy of spot resources should be carefully designed and the impact on both present and future should be considered. For the present, the cloud provider can set a higher price to gain extra revenue. For the future, higher price will shift more requests with lower prices to later time and reduce the revenue of future. Meanwhile, the quality of service should be considered either since bad QoS will incur loss of potential users. In this paper, we present a demand curve to model the impact of pricing on the present and future revenue. Then we formulate the revenue maximization problem as a time-average optimization problem. Next, since this basic model fails to provide information of service delay, we extend it to a more generalized one that ensures the worst-case delay of user requests. While the future knowledge of arrival requests is unknown, it is necessary to design online algorithms for the optimization problems. We apply Lyapunov optimization framework and design an efficient online algorithm which dose not require any future knowledge of requests arrival. Evaluations based on real-life datacenter workload and Amazon EC2 Spot Price illustrate efficiency of our algorithms.

Traditional Virtual Machine Monitor (VMM) virtualizes some devices and instructions, which induces performance overhead to guest operating systems. Furthermore, the virtualization contributes a large amount of codes to VMM, which makes a VMM prone to bugs and vulnerabilities.

In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily launch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, Footprint, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this RSU. We design a location-hidden authorized message generation scheme for two objectives: first, RSU signatures on messages are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second, two authorized messages signed by the same RSU within the same given period of time (temporarily linkable) are recognizable so that they can be used for identification. With the temporal limitation on the linkability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a location-hidden trajectory for location-privacy-preserved identification by collecting a consecutive series of authorized messages. Utilizing social relationship among trajectories according to the similarity definition of two trajectories, Footprint can recognize and therefore dismiss “communities” of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of Footprint.

The study of today's Cyber-Physical System (CPS) has been an important research area. Internet Data Centers (IDCs) are energy consuming CPSs that support the reliable operations of many important online services. Along with the increasing Internet services and cloud computing in recent years, the power usage associated with IDC operations had been surging significantly. Such mass power consumption has brought extremely heavy burden on IDC operators. Recently there are extensive research on power management for IDCs. While most work only consider about dynamical optimization of IDC under electricity markets, the reaction of IDC toward electricity market has been overlooked. Due to the fact that IDCs are usually large-volume users in the electricity market, they might have market power to affect the electricity price. In this paper, we study how to address the challenge of interactions between IDC operation and electricity market price. To this end, we propose a supply function to model the market power of IDC and formulate a total electricity cost minimization problem as a non-linear programming. In order to design efficient solution method, we transform the optimization problem to a quadratic programming. Extensive performance evaluations demonstrate that the proposed method can effectively minimize the total electricity cost of IDCs by adaptively handling the interaction between IDCs and smart grid.

Existing RFID Privacy-Preserving Authentication (PPA) solutions mainly focus on the design of crypto based interactive protocols between readers and tags. Although the cryptographic mechanisms enable randomization and enhance protocol-level privacy, the access mode in RFID systems is less random and may leak private information. We introduce anew attack based on such privacy leakage in access mode, where we show that the mainstream RFID PPA protocols, including the linear, tree-based, and synchronization-based solutions, are not private. We also show that this new attack is easy to conduct, e.g., we can track tags that employ typical tree-based PPA protocols without the need of compromising tags. We discuss the applicability of the attack. Moreover, we provide useful recommendations to strengthen existing PPA protocols in defending against such attacks. The simulation results demonstrate the practicability and effectiveness of this attack.

Prior work on anti-collision for Radio Frequency IDentification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of collisions, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6 times improvement on the identification throughput in a large-scale dense reader environment.

How to reasonably allocate and schedule resources of wireless sensor system to maximum its potential capability has been an important area for research. In this paper, we focus on the Optimal Sampling Frequency Assignment (OSFA) in a real-time wireless sensor networks (RTWSN). An appropriate OSFA should both guarantee a good quality of real-time service and efficiently utilize the limited network resources as well. We propose a distributed optimization algorithm, called Lazy Schema (LySa), to obtain the optimal sampling rates of source nodes with low cost. The central idea is that redundancy reporting and constant adjust step size result in the excessive communicate overhead in iteration process. LySa adopts self-adaptive reporting rates and dynamically adjusts step size to reduce the traffic cost and accelerate the convergence of optimal solution. We have evaluated LySa together with related mainstream algorithms. The results demonstrate that LySa outperforms current state-of-art approaches in terms of low cost, high efficiency and scalability in RTWSN.

Cardinality estimation and tag authentication are two major issues in large-scale Radio Frequency Identification (RFID) systems. While there exist both per-tag and probabilistic approaches for the cardinality estimation, the RFID-oriented authentication protocols are mainly per-tag based: the reader authenticates one tag at each time. For a batch of tags, current RFID systems have to identify them and then authenticate each tag sequentially, incurring large volume of authentication data and huge communication cost. We study the RFID batch authentication issue and propose the first probabilistic approach, termed as Single Echo based Batch Authentication (SEBA), to meet the requirement of prompt and reliable batch authentications in large scale RFID applications, e.g., the anti-counterfeiting solution. Without the need of identifying tags, SEBA provides a provable probabilistic guarantee that the percentage of potential counterfeit products is under the user-defined threshold. The experimental result demonstrates the effectiveness of SEBA in fast batch authentications and significant improvement compared to existing approaches.

Counting or estimating the number of tags is crucial for RFID system. Researchers have proposed several fast cardinality estimation schemes to estimate the quantity of a batch of tags within a short time frame. Existing estimation schemes scarcely consider the privacy issue. Without effective protection, the adversary can utilize the responding signals to estimate the number of tags as accurate as the valid reader. To address this issue, we propose a novel privacy-preserving estimation scheme, termed as MEAS, which provides an active RF countermeasure against the estimation from invalid readers. MEAS comprises of two components, an Estimation Interference Device (EID) and two well-designed Interference Blanking Estimators (IBE). EID is deployed with the tags to actively generate interfering signals, which introduce sufficiently large estimation errors to invalid or malicious readers. Using a secret interference factor shared with EID, a valid reader can perform accurate estimation via two IBEs. Our theoretical analysis and simulation results show the effectiveness of MEAS. Meanwhile, MEAS can also maintain a high estimation accuracy using IBEs.

Complex web applications are usually served by multi-tier web clusters. With the growing cost of energy, the importance of reducing power consumption in server systems is now well-known and has become a major research topic. However, most of previous works focused solely on homogeneous clusters. This paper addresses the challenge of power management in Heterogeneous Multi-tier Web Clusters. We apply Generalized Benders Decomposition (GBD) to decompose the global optimization problem into small sub-problems. This algorithm achieves the optimal solution in an iterative fashion. The simulation results show that our algorithm achieve more energy conservation than the previous works.

In RFID systems, the reader is unable to discriminate concurrently reported IDs of tags from the overlapped signals, and a collision happens. Many algorithms for anticollision are proposed to improve the throughput and reduce the latency for tag identification. Existing anti-collision algorithms mainly employ CRC based collision detection functions for determining whether the collision happens. Generating CRC codes, however, requires complicated computations for both RF tags and readers, and hence incurs non-trivial time consumption, becoming the bottleneck. In this study, we design a Quick Collision Detection (QCD) scheme based on the bitwise complement function plus collision preamble, which significantly reduces the number of gates for computation and facilitates to simplify the IC design of RFID tags. The QCD scheme does not require any modification on upperlevel air protocols, so it can be seamlessly adopted by current anti-collision algorithms. Through comprehensive analysis and simulations, we show that QCD improves the identification efficiency by 40%.

Software rejuvenation is a preventive and proactive technology to counteract the phenomenon of software aging and system failures, and to improve the system reliability. In this paper we present a mixed software rejuvenation policy for an operational software system with multiple degradation states, which considers both the history information and the current running state. By this policy, the system is rejuvenated when it achieves to a degradation threshold or it comes to the pre-determined rejuvenation interval. For comparison, standard rejuvenation policy is also discussed. Continuous-time Markov chains are used to describe the multiple degradation states model. To evaluate these polices expediently, we utilize deterministic and stochastic Petri nets (DSPN) to solve the models. Numerical results show that the deployment of software rejuvenation in the system leads to significant improvement in availability and throughput. And the mixed rejuvenation policy is better than the standard rejuvenation policy.

Localization is a crucial service for various applications in wireless sensor networks (WSNs). Although most researches assume stationary nodes, sensor mobility can enrich the application scenarios. Existing dynamic localization approaches require high seed density or incur a large communication overhead. In order to address these problems, we propose an efficient rang-free localization algorithm, EUL, which utilizes the relationship between neighboring nodes to estimate their possible location boundaries. Our algorithm not only allows all the nodes to remain static or move freely but also reduces the dependence on seeds, which achieves a uniform energy distribution to address the excessive energy drain around seeds and lengthen the network lifetime. We have evaluated EUL together with other major dynamic localization approaches. Simulation results show that EUL outperforms existing approaches in terms of accuracy under many different mobility conditions.

Wireless sensor networks have become a promising technology in monitoring physical world. In many applications with wireless sensor networks, it is essential to understand how well an interested area is monitored (covered) by sensors. The traditional way of evaluating sensor coverage requires that every point in the field should be monitored and the sensor network should be connected to transmit messages to a processing center (sink). Such a requirement is too strong to be financially practical in many scenarios. In this study, we address another type of coverage problem, sweep coverage, when we utilize mobile nodes as supplementary in a sparse and probably disconnected sensor network. Different from previous coverage problem, we focus on retrieving data from dynamic Points of Interest (POIs), where a sensor network does not necessarily have fixed data rendezvous points as POIs. Instead, any sensor node within the network could become a POI. We first analyze the relationship among information access delay, information access probability, and the number of required mobile nodes. We then design a distributed algorithm based on a virtual 3D map of local gradient information to guide the movement of mobile nodes to achieve sweep coverage on dynamic POIs. Using the analytical results as the guideline for setting the system parameters, we examine the performance of our algorithm compared with existing approaches.

Efficient aggregation of data collected by sensors is crucial for a successful application of wireless sensor networks (WSNs). Both minimizing the energy cost and reducing the time duration (or called latency) of data aggregation have been extensively studied for WSNs. Algorithms with theoretical performance guarantees are only known under the protocol interference model, or graph-based interference models generally. In this paper, we study the problem of designing time efficient aggregation algorithm under the physical interference model. To the best of our knowledge, no algorithms with theoretical performance guarantees are known for this problem in the literature. We propose an efficient algorithm that produces a data aggregation tree and a collision-free aggregation schedule. We theoretically prove that the latency of our aggregation schedule is bounded by O(R+Δ) time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. In addition, we derive the lower-bound of latency for any aggregation scheduling algorithm under the physical interference model. We show that the latency achieved by our algorithm asymptotically matches the lower-bound for random wireless networks. Our extensive simulation results corroborate our theoretical analysis.

Radio frequency identification (RFID) technologies are on their highway to pervasive usage. However privacy protection is still an important problem since RFID tags attached to items are so cost constrained. Privacy preserving authentication approaches are proposed to authenticate tags without private information leaking. Previously designed approaches based on synchronization seeks O(1) complexity. While these synchronization based methods are efficient in normal case, they have weak points when desynchronized. When maliciously scanned, information stored in tag and reader goes farther and farther away from each other. An adversary can utilize this point to track a tag. We propose an enhanced synchronization approach for RFID private authentication, ESP, to solve this problem. ESP can eliminate the problem caused by desynchronization attack and help detecting replay attack. Analysis shows that ESP enhances privacy protection while still maintaining the authentication efficiency.

Privacy protection is increasingly important during authentications in Radio Frequency Identification (RFID) systems. In order to achieve high-speed authentication in large-scale RFID systems, researchers propose tree-based approaches, in which any pair of tags share a number of key components. Such designs, being efficient, often fail to achieve forward secrecy and resistance to attacks, such as compromising and desynchronization. Indeed, these attacks may still take effect even after a tag successfully finishes the authentication and key-updating procedure. To address the issue, we propose a lightweight RFID private authentication protocol, RWP, based on the random walk concept. RWP also provides the forward security and temporal resistance to the tracking attack. The analysis results show that RWP effectively enhances the security protection for RFID private authentication, and increases the authentication efficiency from O(logN) to O(1).

With the chip multi-processor (CMP) being more and more widespread used in the laptop, desktop and data center area, the power-performance scheduling issues are becoming challenges to the researchers. In this paper, we propose a multi-objective fuzzy genetic algorithm to optimize the energy saving scheduling tasks on heterogeneous CMP system. According to the characteristic of heterogeneous CMP system, we present a novel encoding and decoding scheme of genetic algorithm, improve the crossover operator and the mutation operator. Based on that, we improve the genetic algorithm architecture by using the relative fuzzy membership grade fitness and the elitist strategy. Simulation results demonstrate that using our algorithm can save both the execution time and system energy cost at the same time.

In this paper, we address the problem of joint channel assignment, link scheduling, and routing for throughput optimization in wireless networks with multi-radios and multi-channels. We mathematically formulate this problem by taking into account the interference, the number of available radios the set of usable channels, and other resource constraints at nodes. We also consider the possible combining of several consecutive channels into one so that a network interface card (NIC) can use the channel with larger range of frequencies and thus improve the channel capacity. Furthermore, we consider several interference models and assume a general yet practical network model in which two nodes may stillnotcommunicate directly even if one is within the transmission range of the other. We designed efficient algorithm for throughput (or fairness) optimization by finding flow routing, scheduling of transmissions, and dynamic channel assignment and combining. We show that the performance, fairness and throughput, achieved by our method is within a constant factor of the optimum. Our model also can deal with the situation when each node will charge a certain amount for relaying data to a neighboring node and each flow has a budget constraint. Our extensive evaluation shows that our algorithm can effectively exploit the number of channels and radios. In addition, it shows that combining multiple channels and assigning them to a single user at some time slots indeed increases the maximum throughput of the system compared to assigning a single channel.

Energy efficient routing and power control techniques in wireless ad hoc networks have drawn considerable research interests recently. In this paper, we address the problem of energy efficient reliable routing for wireless ad hoc networks in the presence of unreliable communication links or devices or lossy wireless link layers by integrating the power control techniques into the energy efficient routing. We consider both the case when the link layer implements a perfect reliability and the case when the reliability is implemented through the transport layer, e.g., TCP. We study the energy efficient unicast and multicast when the links are unreliable. Subsequently, we study how to perform power control (thus, controlling the reliability of each communication link) such that the unicast routings use the least power when the communication links are unreliable, while the power used by multicast is close to optimum. Extensive simulations have been conducted to study the power consumption, the end-to-end delay, and the network throughput of our proposed protocols compared with existing protocols.

Biological systems exhibit remarkable adaptation and robustness in the face of widely changing environments. Currently speaking, we often imitate the properties of biological systems. Based on this thought, it also exists the analogous situation in the WSNs (Wireless Sensor Networks). Survivability is the ability to provide essential services in the presence of attacks and failures, and recover full services in timely manner. The conventional security technologies for WSNs only focus on confidentiality, integrity and authentication and can not provide survival services. The WSNs survivability depends most critically on base station that attaches WSN to outside networks including Internet. Thus, to increase the survivability, one Survivable model for base station in WSNs is presented with rejuvenation technology, where it is designed to provide continued useful services in face of attacks, failure or accidents and to prevent the intruders’ attempts in their attack. This model is described and analyzed by semi-Markov Process for survivability. Finally, according to the experimental results, current model has the feasibility to enhance the survivability level for WSNs.

With the increased interest in the Web services composition, more and more enterprises and businesses depend on this paradigm. Open, distributed and dynamic properties of the schema, there is a pressing need for secure services in daily transactions. Orchestration and choreography language provide basic services standards and interaction, collaboration, and negotiation standards among services, but they are not give any secure manners or secure operation styles and specifications. Despite the interest of such security mechanisms, a formal module of them is still lacking. For giving general guide to implement secure orchestration and choreography language, we give a formal approach to carry out those goals. To this target, we emphasize on those by designing an extension of the Spi calculus with Secure Global Calculus. The Spi calculus precisely identifies orchestration secure properties of each principal from a local viewpoint. The secure global calculus describes an interaction secure choreography scenario from a vantage point of view. We called our method SpiG4WSC calculus. We believe that the combination of strong practical needs for dynamic secure Web services composition and the theoretical foundations will lead to a bridge between practice and theories. The contribution of this paper are (1) giving the syntax and semantic of SpiG4WSC calculus; (2)applying the calculus to give a model to presenting the secure orchestration, emphasizing on the formal basis for secure services; (3)describing the secure choreography, giving the formal frame for interaction processes.

Service Oriented Architecture (SOA) and Service Oriented Computing (SOC) are prevailing paradigms for sharing and reusing resources. Service composition is a methodology widely used in SOA and SOC to build new value-added services on primitive services on-the-fly to support online Business-to-Business collaborations. Requirements of customers to these composite services include functionality and non-functionality. Since many services can have the similar functionality, the non-functionality of composite services, such as Quality of Services (QoS), is the important metrics to distinguish a service from each other and find an optimal program to meet the requirements of customers. This paper firstly proposes a system model from the view of resource and value, and then we introduce an improved heuristic algorithm for the selection of composite services with multiple constraints. The simulation experiments show an outperforming result of proposal algorithm in both utility performance and time cost comparing with the other heuristic algorithms.

No abstract available.

There are two important factors in the power-performance issues of chip multi-processor(CMP) system: the execution time of tasks and the system energy consumption. Most of exist energy saving methods are not designed to reduce the system energy while cut the execution time down. This paper represents a multi-objective hybrid genetic algorithm (MHGA) which can make the execution time of tasks minimize while reducing the system power consumption. We analyze the problem of energy saving task scheduling on CMP system and a novel coding scheme of genetic algorithm. Based on that, we improve the crossover and mutation operator of genetic algorithm. We propose the multi-objective genetic algorithm by using simulated annealing algorithm to enhance the search ability. Simulation results demonstrate that using our algorithm can make the efficiency of task scheduling on CMP increase, make both the execution time of task and energy consumption of system decrease.

Nowadays, smart spaces occupy an essential part of ubiquitous computing environment. The spaces integrated with wireless sensors networks, actuators and context-aware services become part of our daily life. Smart spaces are equipped with a large number of wireless sensors that aim to collect large quantities of context information, during the process, there exists a large amount of collisions and energy consumption. Therefore, this paper provides a novel multi-rate based local framing pre-schedule scheme to further reduce collisions and improve energy efficiency in CSMA/TDMA hybrid MAC layer of wireless sensor network. This MAC combines CSMA and TDMA functionalities together while obviates their shortcomings. Having been assigned, slot 0 is preserved as the pre-schedule slot, to inform neighbor nodes the schedule of the senders. During the pre-schedule slot, each node knows exactly the schedule of other neighbor nodes. Multi-rate and power scaling are applied to achieve further energy saving by adpoting an acceptable rate rather than maximum rate. Data rate is dynamically adjusted according to the traffic load of sending nodes, in an energy efficient data rate, to save energy. Being compared with Z-MAC in terms of performances, local framing pre-schedule and multi-rate in this experiment achieved further energy efficiency. Index Terms--MAC, CSMA, TDMA, Mult-Rate, Wireless Sensor Networks

Insulin pump system is a safety-critical embedded system controlling the amount of injection of insulin to diabetics based upon their blood glucose levels, and the high reliability of the software used in the pump is crucial. One way to achieve the high reliability of software is to build an accurate and complete model through effective analysis and specification, and to implement the system based upon the specification. In this paper, we describe how the SOFL formal engineering method is applied to develop a specific insulin pump system in practice. In particular, we focus on the issue of how the three-step modeling approach advocated by the SOFL method, which includes informal, semi-formal, and formal specifications, is utilized to obtain a precise and valid specification of the embedded software for the insulin pump system. We also discuss how the specification benefits the implementation of the system, and report our experience and lessons learned.

By using personal information in a pervasive computing environment, context-aware applications can provide appropriate services for people. This personal information is often involved in personal privacy. In order to protect personal privacy concerns about personal information, privacy role is proposed to control access personal information. We also construct an information system about the privacy decision of personal information disclosure based on people's interaction history. In the initial period of personal information disclosure, the privacy decision is made by people and the information system is constructed based on the decision data. Then privacy disclosure policies are extracted from this information system using rough set theory. According to deducing from the privacy disclosure policies and people's context information, the contextaware application is assigned to an adequate privacy role. It reduces the distraction of privacy decision for people. A case study further shows the proposed method is effective. Finally, it provides about the overload performance of privacy role analysis personaengine.

In pervasive environment, context-aware service provider can use personal information to customize the adequate services for end users. Personal information is people's privacy concern. Therefore, people need privacy control methods. In this paper, we analyzed privacy control from two aspects: personal privacy model about information disclosure and the function of context-aware service provider. According to the analysis, we designed the components about context-aware privacy control in order to minimize the burden of personal privacy decision about personal information disclosure. The simulation experiment shows that it is possible method for the proposed privacy protection mechanism. Finally, we also proposed conceptual context-aware architecture to control personal information disclosure.

According to the characteristics of the operational behavior and runtime state of application sever, the resource consumption time series are observed and modeled by fuzzy wavelet network (FWN) with fuzzy logic inference and learning capability. The objective is to model the extracted data series of systematic performance parameters to predict software aging in application server. The dimensionality of input variables of FWN is reduced by principal components analysis (PCA), and the structure and parameters of FWN are optimized with adaptive genetic algorithm (GA). Judging by the model, we can get the aging threshold before application server failed and preventively maintenance the application server before systematic parameter value reaches the threshold. The experiments are carried out to validate the efficiency of the proposed model and show that the aging prediction model based on FWN with adaptive genetic algorithm is superior to the neural network (NN) model and wavelet network (WN) model in the aspects of convergence rate and prediction precision

Content protection is now becoming more and more important for digital rights management (DRM), which involves rights embedding, identification, rights validation digital multimedia resource is popular in the real world, how to protect multimedia content from be violated without rights control is an important thing especially to resist copy-spread kind violation. In this paper, an novel approach for multimedia rights management is proposed based on partial encryption method, which can control multimedia resource played in a rights-constraint environment, which can protect multimedia resource from being copying and spreading, and in the authorization usage environment, the protected multimedia is properly played as normal, however once the resource is beyond the authorization environment, the protected resource will not be played correctly. Experiments showed our proposed partial encryption approach was efficient with real-time quality of service, which was suitable for online multimedia streaming in content delivery network.