ABSTRACT Recent studies have integrated reputation mechanisms into practical byzantine fault tolerance (PBFT) by evaluating nodes based on their historical performance‐nodes with superior performance receive higher reputations, while those with poorer performance receive lower reputations. Typically, the node with the highest reputation is selected as the primary to reduce the frequency of time‐consuming view changes—a protocol employed to rectify issues arising from a faulty primary. However, such reputation‐based approaches face two significant challenges: maintaining real‐time reputation accuracy requires updating node reputations after each consensus round, leading to considerable computational overhead, and selecting the highest‐reputation node renders it a prime target for malicious attacks. To address these challenges, we propose the adaptive reputation‐based PBFT algorithm with verifiable random function (VRF)‐driven view changes (ARVPBFT). ARVPBFT incorporates an adaptive reputation mechanism that dynamically adjusts the frequency of reputation updates based on view changes, substantially reducing computational overhead. Moreover, by integrating VRFs into the view‐change protocol, ARVPBFT ensures an unpredictable and fair selection among high‐reputation nodes, thereby further enhancing system security. Theoretical analysis and simulation results demonstrate that ARVPBFT significantly outperforms existing algorithms, ultimately achieving a more stable and efficient consensus mechanism.

To improve the transaction processing rate of blockchain, the existing sharding technology divides a blockchain network into subgroups (i.e., shard), and realizes intra-shard transaction processing locally and inter-shard transaction processing via intermediary accounts (i.e., brokers). However, if the inter-shard processing fails due to malicious brokers, the sharding technology has to carry out a rollback process for blockchain safety such that the transaction processing rate is decreased significantly. In this paper, we present a novel inter-shard transaction processing scheme called Rollback-free Blockchain (RF-Chain for short), which can process inter-shard transaction securely without rollbacks in the presence of malicious brokers. To this end, RF-Chain includes a Deposit Mechanism on a global smart contract, so as to refund the transaction victim if a malicious broker behavior is detected. We developed a RF-Chain prototype and conducted abundant experiments using real Ethereum transaction data. Compared with the existing rollback-based schemes, RF-Chain increases the transaction throughput by 98.3% and decreases the transaction confirmation latency by 85.1% in the presence of malicious activities using 3.7% communication overhead. Thus, RF-Chain can effectively mitigate malicious broker behaviors and achieve lightweight rollback-free processing.

In modern medicine, the widespread use of medical imaging has greatly improved diagnostic and treatment efficiency. However, these images contain sensitive personal information, and any leakage could seriously compromise patient privacy, leading to ethical and legal issues. Federated learning (FL), an emerging privacy-preserving technique, transmits gradients rather than raw data for model training. Yet, recent studies reveal that gradient inversion attacks can exploit this information to reconstruct private data, posing a significant threat to FL. Current attacks remain limited in image resolution, similarity, and batch processing, and thus do not yet pose a significant risk to FL. To address this, we propose a novel gradient inversion attack based on sparsified gradient matching and segmentation reorganization (SR) to reconstruct high-resolution, high-similarity medical images in batch mode. Specifically, an $L_{1}$ loss function optimises the gradient sparsification process, while the SR strategy enhances image resolution. An adaptive learning rate adjustment mechanism is also employed to improve optimisation stability and avoid local optima. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in both visual quality and quantitative metrics, achieving up to a 146% improvement in similarity.

Sharding enhances the Internet of Things (IoT) blockchain scalability by parallel transaction (TX) processing, yet cross-shard TXs and dynamic workload imbalance persist as key bottlenecks. Existing solutions employ brokers that have segmented accounts across multiple shards to efficiently handle cross-shard TXs. Nevertheless, they rely on preset optimal accounts as brokers, which may not be feasible in dynamic IoT blockchains where TX workloads are unpredictable. To this end, this paper proposes ABChain, a blockchain sharding protocol for efficient state synchronization with two key components. One is a balanced account partitioning method which allocates accounts to the shards with the highest degree of correlation, and the other is an adaptive broker framework which includes dynamic broker selection from historically active accounts, seamless broker updates via account splitting/aggregation, and dual broker queues to minimize cross-shard TX overhead. The theoretical analysis shows that the former can reduce the number of cross-shard TXs and mitigate workload imbalance, while the latter can efficiently handle cross-shard TXs and adapt to the dynamic blockchain environments. Experimental evaluations demonstrate ABChain’s superiority. Compared to state-of-the-art protocols, it improves throughput by 33.4%, reduces cross-shard TX rate by 52.3%, and lowers confirmation latency by 67.7%.

Detection of anomalous event at the edge of network has attracted wide attention from both academic and industrial fields recently. During the detection process, several primary sensing attributes are jointly utilized to determine whether an anomalous event occurs or not. However, as the primary attributes of some Internet of Things (IoT) devices are easy missing due to the natural wear and they cannot be timely and accurately accessed, the event detection efficiency is very low. In view of this, our work introduces a digital twin (DT)-assisted detection technology for anomaly identification in a device-edge–cloud architecture. Specifically, for an edge server with missing primary attributes, the probability of anomalous event occurring on it can be calculated by analyzing the primary attribute fusion values of its adjacent edge servers. As a result, it is unnecessary to carry on detection in advance on the edge servers with a low anomaly occurring probability, efficiently reducing the detection cost. For the remaining edge servers with a high probability, the primary attributes with high accuracy are migrated by considering the difference on the historical value variant trend and the fusion effect. Based on this, a decision tree will be built in the integrated DT model for anomalous event detection in advance. Further, the cloud collects other relevant attributes to build a random forest for the final identification and judgment of anomalous events. Experimental results show that our method achieves a higher detection performance in terms of energy consumption, detection time, and accuracy by at least 37.1%, 39.5%, and 1.82% compared to the baselines.

Model Inversion (MI) attacks based on Generative Adversarial Networks (GAN) aim to recover private training data from complex deep learning models by searching codes in the latent space. However, this method merely searches in a deterministic latent space, resulting in suboptimal latent codes. Additionally, existing distributional MI schemes assume that an attacker can access the structures and parameters of the target model, which is not always feasible in practice. To address these limitations, this paper proposes a novel Distributional Black-Box Model Inversion (DBB-MI) attack by constructing a probabilistic latent space for searching private data. Specifically, DBB-MI does not require the target model’s parameters or specialized GAN training. Instead, it identifies the latent probability distribution by integrating the output of the target model with multi-agent reinforcement learning techniques. Then, it randomly selects latent codes from the latent probability distribution to uncover private data. As the latent probability distribution closely mirrors the target privacy data in the latent space, the recovered data effectively leaks the privacy of the target model’s training samples. Extensive experiments conducted on diverse datasets and networks demonstrate that our DBB-MI outperforms state-of-the-art MI attacks in terms of attack accuracy, K-nearest neighbor feature distance, and peak signal-to-noise ratio.

Interoperability is a fundamental challenge for longenvisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves lowcost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design an incentive scheme based on smart contracts to stimulate the honest behavior of two hosts, bypassing the requirement of the number of TEE and large memory need. We examine the conditions to guarantee the uniqueness of Nash Equilibrium via Game Theory. Furthermore, an extended optimistic concurrency control protocol is designed to ensure the correctness of concurrent contracts execution. We formally prove the security of IvyCross in the Universal Composability (UC) framework and implement a prototype atop Bitcoin, Ethereum, and FISCO BOCS. Extensive experimental results on end-to-end performance and concurrency control demonstrate the efficiency and practicality of IvyCross.

Since primary nodes play a critical leadership role in PBFT (Practical Byzantine Fault Tolerance) consensus algorithm, it is mandatory to perform an update process for eliminating the malicious primary nodes so as to maintain the security level of PBFT. As it is convenient to calculate node reputations, most of existing updating schemes select the node with the highest reputation as the primary node in every consensus round. However, the round-by-round reputation update mechanism inevitably increases the communication overhead and consensus latency. To reduce the number of update times, we propose an algorithm based on an adaptive reputation mechanism, named Commit Block PBFT or CBPBFT for short. In this mechanism, a node’s reputation is determined by the number of its committed blocks during the most recent period as a primary node. As the reputation update adaptively occurs according to the number of blocks committed by the primary node, the mechanism significantly decreases the frequency of reputation updates, and hence increases the consensus performance. Experimental results show that, CBPBFT reduces the probability of a Byzantine node gaining leadership by 15% and decreases communication latency by 15% to 32% in comparison with the state-of-the art.

Incentive plays an important role in mobile crowdsensing (MCS), as it impels mobile users to participate in sensing tasks and provide high-quality sensing data. However, considering the privacy (including identity privacy, sensing data privacy, and reputation value privacy) and practicality (including reliability, quality awareness, and efficiency) issues in practice, it is a challenge to design such an effective incentive scheme for MCS applications. Existing studies either fail to provide adequate privacy-preserving capabilities or have low practicality. To address these issues, we propose a scheme called BRRV in MCS which relies on two rounds of range reliability assessment to guarantee the reliability of data while achieving privacy preservation. In addition, we also present a lightweight scheme called LRRV in MCS which relies on a single round of range reliability assessment to guarantee the reliability of data while achieving lightweight and privacy preservation. Moreover, to fairly stimulate participants, constrain participants' malicious behavior, and improve the probability of high-quality data, we design a quality-aware reputation-based reward and penalty strategy to achieve dual incentives (including money incentives and reputation incentives) for participants. Furthermore, comprehensive theoretical analysis and experimental evaluation demonstrate that our proposed schemes are significantly superior to the existing schemes in several aspects.

For cybersecurity, privacy protection in big data has received more and more attention and research. Differential privacy is one of the important privacy protection methods, and our work pays attention to differential privacy based on the dynamic taxonomy tree, which can protect the publishing of set-valued data effectively. We propose the optimized dynamic taxonomy tree (ODTT) algorithm as a better and more general way to protect privacy in set-valued datasets. It makes better use of data and reduces noise compared to other privacy-preserving algorithms that use taxonomy tree partitioning. The previous algorithm did not make full use of the characteristics of the dataset when constructing the taxonomy tree, so a 2-itemset’s matrix is used in the proposed algorithm to increase the pseudoempty nodes and reduce the addition of noise. More importantly, we apply the consistency constraint method to the construction of the ODTT algorithm. This retains more statistical characteristics of the original dataset by constraining the noise counts in the leaf partitions of the partition tree. Furthermore, ODTT is extended to deal with dynamic datasets. Finally, we compare the proposed ODTT algorithm with the state-of-the-art CDTT algorithm, by performing a series of experiments and using some evaluation metrics. Experimental results show that ODTT is more general and has higher usability while satisfying the security of differential privacy.

Abstract Bitcoin is a popular decentralized cryptocurrency, and the Bitcoin network is essentially an unstructured peer-to-peer (P2P) network that can synchronize distributed database of replicated ledgers through message broadcasting. In the Bitcoin network, the average clustering coefficient of nodes is very high, resulting in low message propagation efficiency. In addition, average node degree in the Bitcoin network is also considerably large, causing high message redundancy when nodes use the gossip protocol to broadcast messages. These may affect message propagation speed, hindering Bitcoin from being applied to scenarios of high transactional throughputs. To illustrate, we have collected single-hop propagation data of transactions of 366 blocks from Bitcoin Core. The analysis results show that transaction verification and network delay are two major causes of low transaction propagation efficiency. In this paper, we propose a novel P2P network structure, called local clique network (LCN), for message broadcasting in the Bitcoin network. Specifically, to reduce transaction validation latency and message redundancy, in LCN local nodes (logically) form cliques, and only a few nodes in a clique broadcast messages to the other cliques, instead of each node sending messages to its neighboring nodes. We have conducted extensive experiments, and the results show that message redundancy is low in LCN, and message propagation speed increases significantly. Meanwhile, LCN exhibits excellent robustness when average node degree remains high in the Bitcoin network.

With the digital transformation of the energy industry, energy blockchain is playing an important role in application areas, such as energy data sharing and distributed power trading. In this process, the use of energy data is a top priority. Federated learning (FL) can enable the analysis and computation of energy data while protecting their privacy. However, traditional FL relies on a central server and parties involved are not fully trusted. In energy blockchain environment, FL also faces data poisoning attacks launched by energy departments, besides, the supervisory committee carrying out checking models can launch deception attacks. Therefore, we propose a game theory-based incentive mechanism for collaborative security of FL in energy blockchain environment, which can discourage nodes from taking malicious behaviors in iterative training of FL. First, we propose an FL model in energy blockchain environment, which can protect privacy and achieve collaborative security. Considering that game theory can be used to analyze the strategies of participants, we build a game model with energy departments and supervisory committee as players and design our incentive mechanism based on game theory, which is implemented by smart contracts. Even if the accuracy of model checking algorithm is low, malicious behaviors in FL can be reduced by using our incentive mechanism. In particular, we prove that our mechanism can lead game model to a Nash equilibrium (NE) that achieve collaborative security. Security analysis and experimental evaluation show that our incentive mechanism is feasible in energy blockchain with robustness, reliability, and low complexity.

In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this article proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.

Mobile crowdsensing (MCS) refers to a group of mobile users utilizing their sensing devices to accomplish the same sensing task. However, in vehicular networks, how to evaluate the reliability of sensing vehicles and achieve lightweight privacy preservation are urgent issues. Therefore, this paper proposes a lightweight privacy preservation scheme with efficient reputation management (PPRM) for MCS in vehicular networks. Specifically, we design a lightweight privacy-preserving sensing task matching algorithm which can preserve the location privacy, identity privacy, sensing data privacy, and reputation value privacy while reducing communication and computation overheads of sensing vehicles. In particular, to prevent reputation values from being forged and select reliable sensing vehicles, we present a privacy-preserving reputation value equality verification algorithm to verify reputation values and a privacy-preserving reputation value range proof algorithm to choose sensing vehicles. Afterwards, a three-factor reputation value update algorithm is constructed to efficiently and accurately update the reputation values for sensing vehicles. Simulations are conducted to demonstrate the performance of the PPRM scheme, and the results show that the PPRM scheme significantly outperforms the existing schemes in security and robustness aspects.

Based on the Threshold Predicate Encryption (TPE), the biometric authentication schemePassBioaims to correctly authenticate genuine end-users without leaking their biometric privacy information. However, this article proposes two impersonation attacks toPassBioby merely sending very few query messages. Specifically, an attacker is able to cheat the authentication server with probability 50% by sending the server a random query, or almost 100% by sending the server a collusion of old genuine queries, without being identified. Moreover, in order to defeat the impersonation attacks, this article presents a Verifiable Threshold Predicate Encryption (VTPE) scheme which includes three components: (1) a multi-segment TPE for reducing the computational cost and communication overhead significantly; (2) a segment-wise watermarking for defeating the random attacks; and (3) a challenge-response mechanism for defeating the replay and collusion attacks. In addition, the watermarking also creates a secure channel between the querying user and the server. The experiments on both simulated feature vectors and real face images demonstrate that the present attacks and countermeasures are effective and efficient.

In an acceleration-based Secure Device Pairing (SDP) scheme, two unauthenticated devices continuously measure their own acceleration. If the similarity of their measurements are sufficiently high, the devices will build a secure communication channel assume that it is hard for any attacker to estimate their measurements in real-time. This paper demonstrates that the assumption does not hold and further proposes an effective Man-in-the-Middle (MitM) attack on acceleration-based SDP schemes. That is to say, an MitM adversary is able to quickly estimate the acceleration measurements of the target devices with automatic visual tracking technologies, and then compromise the device’s communication channel by impersonating the target devices with the estimated measurements. The present attack is extensively evaluated on acceleration-based SDP schemes in indoor and outdoor environments. The evaluation results show that the device’s acceleration can be estimated with high accuracy in real time. Thus, the present MitM attack is practical to defeat the acceleration-based SDP schemes.

Unmanned Aerial Vehicle (UAV) networks have huge potential for a variety of military and civilian uses, such as intelligent transportation system, smart city, and so on. The 6th Generation (6G) communication technology is expected to provide 3-Dimensional (3D) wireless coverage and greatly improve the performance of UAV networks. The UAV-to-UAV (U2U) message exchange (or message exchange for short) is an important basis of multi-UAV cooperation. However, due to the unique characteristics of UAV networks, the U2U messages (or messages for short) are vulnerable to both the external and internal attackers. In this work, we propose a Lightweight Trustworthy Message Exchange (LTME) scheme for UAV networks by efficiently aggregating the cryptography and trust management technologies. In the LTME scheme, a centralized Ground Control Station (GCS) periodically updates the reputation levels of registered UAVs (or UAVs for short) and securely distributes secret values to the UAVs. Based on the received secret values, each trustworthy broadcasting UAV can generate its encrypted messages so that only trustworthy receiving UAVs can decrypt them, and each trustworthy receiving UAV can accurately judge whether the received messages and the corresponding broadcasting UAVs are trustworthy in a lightweight manner. Furthermore, we present a simplified LTME (sLTME) scheme and conduct a comprehensive theoretical analysis and simulation evaluation for the LTME and sLTME schemes. The results demonstrate that the proposed schemes can provide rich functionality and strong robustness with low computation and communication overheads, and are significantly superior to the existing schemes in several aspects.

Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal” areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down "data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.

With the development of the Internet, a large amount of transaction data (e.g., shopping records, web browsing history), which represents user data, has been generated. By collecting user transaction data and learning specific patterns and association rules from it, service providers can provide better services. However, because of the increasing privacy awareness and the formulation of laws on data protection, collecting data directly from users will raise privacy concerns. The concept of local differential privacy (LDP), which provides strict data privacy protection on the user side and allows effective statistical analysis on the server side, is able to protect user privacy and perform statistics on sensitive issues at the same time. This paper adopts padding-and-sampling-based frequent oracle (PSFO), combined with an interactive query-response method satisfying local differential privacy, to identify frequent itemsets in an efficient and accurate way. Therefore, this paper proposes FIML, an improved algorithm for finding frequent itemsets in the LDP setting of transaction data. The data collector generates frequent candidate sets based on the results of the previous stage and uses them for querying, and users randomize their responses in a reduced domain to achieve local differential privacy. Extensive experiments on real-world and synthetic datasets show that the FIML algorithm can find frequent itemsets more efficiently with the same privacy protection and computational cost.

Sequential rule mining (SRM) is an alternative to sequential pattern mining (SPM) when dealing with sequence data. SRM has a wide range of applications in numerous data analysis scenarios. Existing SRM algorithms usually discover the entire set of rules in the databases, which makes it not only difficult to analyze results because the discovered set is too large, but also does not consider the user’s expectations and background knowledge. To tackle this problem, researchers have explored related algorithms with different constraints according to their requirements. In this paper, we propose a flexible constraint-based SRM algorithm called ConSRM for discovering only the sequential rules within user-specified time bounds in a sequence database. This algorithm uses an efficient rule-growth method and develops corresponding constraints and pruning strategies to reduce the search space and speed up calculation. Comprehensive experiments were carried out on four real datasets to evaluate the performance (both effectiveness and efficiency) of ConSRM.

Crowdsourcing refers to the employment of workers to complete tasks. Workers will also be rewarded after completing the tasks, which creates two problems. One is how to effectively aggregate the right answers of tasks when the accuracy of workers’ answers varies. The other is how to deal with Sybil attacker, who will get rewards by controlling half or more Sybil workers to influence the Truth Inference. We propose a Truth Discovery with intelligent Sybil defense, called STDEL (Sybil-resistant Truth Discovery by Exploiting the Long-tail Effect). This algorithm can improve the accuracy of Truth aggregation and reduce the rewards by identifying and banning intelligent Sybil workers. Our Experiments on two real world datasets show that STDEL has better performance on accuracy and rewards compared with state-of-the-art Sybil defense Truth Discovery.

Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor–critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.

In this article, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to cloud to deliver telemedicine service. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of patient’s illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analysis to evaluate the efficiency.

With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establishing a trustworthy communication channel, has received extensive attention from security researchers. Previous secure pairing protocols depend on auxiliary peripherals (e.g., displays, speakers) to share the secret message, while for those products featuring with low-price, manufacturers would probably adopt insecure pairing methods to reduce the cost, so the devices may be subject to various attacks. To mitigate such a situation, we design a peripheral-free secure pairing protocol, termed SwitchPairing. Our protocol only requires users to connect the pre-pairing devices to the same power source, then randomly presses and releases the switch to generate a shared secret. It does not require additional peripherals and can defense eavesdropping and replay attacks innately. We implement a prototype via two CC2640R2F development boards and invite volunteers to participate in the experiments about bench-marking security and usability. The result of our experiments show that our protocol can fulfill the security and efficient requirement of various IoT applications.

Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on natural numbers only) or leak information of the underlying data. To tackle these shortcomings, this paper proposes Secure Outsourced Computation on Integers (SOCI) based on PHE and a twin-server architecture. Compared with the existing solutions, SOCI supports computations on encrypted integers (vs. natural numbers) and greatly improves the security and correctness of the computations. Results of theoretical analysis and experimental evaluation show that SOCI outperforms existing solutions in computation and communication efficiencies.

High-frequency itemset mining (HFIM) and high-utility itemset mining (HUIM) aim to discover itemsets with high occurrence and high utility, respectively, in a transaction database. A number of efficient algorithms have been developed to identify these high-utility itemsets (HUIs) or high-frequency itemsets (HFIs). Such algorithms play an increasingly important role in many occasions especially for analysis in commercial enterprises. In this paper, we propose a new model called joint utility and frequency for pattern classification, and two new algorithms, namely UFCgenand UFCfast. Both algorithms are designed to categorize each itemset into different type of patterns by setting the minimum thresholds of utility and frequency. We compare these algorithms on two datasets. The experimental results show that both algorithms can successfully collect three different types of itemsets from all candidate itemsets based on frequency and utility, and the list-based UFCfastalgorithm outperforms the level-wise-based UFCgenalgorithm in terms of execution time.

As a potential application field of the sixth-generation (6G) communication technology and a promising part of massive Internet of Things (IoT), vehicular networks have attracted considerable attention from both academia and industry in recent years, where the cooperative safety applications are a significant branch. It is widely acknowledged that 6G is able to provide high-throughput and low-latency wireless communication capability for vehicular networks, support massive interconnectivity in vehicular networks with diverse service requirements, and significantly improve the performance of vehicular networks. Both trust management and privacy preservation play significant roles in vehicular networks, and there exists a tradeoff between them. For providing a satisfactory solution to balance the trust management and privacy preservation in vehicular networks, we put forward a novel scheme named as BTMPP (which is able to provide both the precise trust management and strong conditional privacy preservation simultaneously) in this article by leveraging the famous bloom filter (BF)-based private set intersection (PSI) technology. Furthermore, the theoretical analysis for the correctness, strong conditional privacy preservation capability, strong robustness, precise trust management, and the other features is detailed, and a series of simulations are conducted. The results reveal that the proposed scheme significantly outperforms the existing schemes in several aspects.

Numerous anonymous authentication schemes are designed to provide efficient authentication services while preserving privacy. Such schemes may easily neglect access control and accountability, which are two requirements that play an important role in some particular environments and applications. Prior designs of attribute-based anonymous authentication schemes did not concentrate on providing full anonymity while at the same time holding public traceability. To address this problem, we formally define and present a new primitive called traceable attribute-based anonymous authentication (TABAA) which achieves (i) full anonymity, i.e., both registration and authentication cannot reveal user’s privacy; (ii) reusable credential, i.e., a registered credential can be repeatedly used without being linked; (iii) access control, i.e., only when the user’s attribute satisfies the access policy can the user be involved in authentication; and (iv) public traceability, i.e., anyone, without help from the trusted third party, can trace a misbehaving user who has authenticated two messages corresponding to a common address. Then, we formally define the security requirements of TABAA, including unforgeability, anonymity, and accountability, and give a generic construction satisfying the security requirements. Furthermore, based on TABAA, we propose the first attribute-based, decentralized, fully anonymous, publicly traceable e-voting, which enables voters to engage in a number of different voting activities without repeated registration.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

Live video programs can bring in better travel experience for subway passengers and earn abundant advertisement revenue for subway operators. However, because the train-trackside channels for video dissemination are easily accessible to anyone, the video traffic are vulnerable to attacks, which may cause deadly tragedies. This paper presents a situation-aware authenticated video broadcasting scheme in the railway network, which consists of train, on-board sensor, trackside global system for mobile communications-railway (GSM-R) device, WiFi access point (AP), and train control center. Specifically, the scheme has four modules: 1) a train uses its on-board sensors to obtain its speed, location, and received signal strength indicator of train-trackside WiFi channel; 2) the train reports these real-time measurements to the railway control center with the legacy GSM-R networks; 3) according to the measurements, the control center or its WiFi AP adaptively customizes the protected codestream bitrate and AP-train handover time; and 4) the train renders the received codestream, which passes the authenticity verification process. As shown in the performance analysis, the present scheme ensures the codestream authenticity and provides high quality of service in the lossy subway WiFi environment.

In recent years, researchers have proposed several secure device pairing schemes that allow mobile devices in close proximity to establish a trusted communication channel between them without sharing any secret in advance. These schemes use the correlation of some physical measurements (magnetic field, acceleration, etc.,) made independently by the two pairing devices to reconcile them. Their security against a Man-in-the-Middle (MitM) attacker relies on the difficulty for the MitM attacker to obtain a measurement data similar to the two pairing devices. As a key step in the reconciliation process, an interlock protocol is used in several recent schemes (e.g., Magpairing and ShaVe) to ensure that the measurement data is not leaked. However, the present paper points out that these schemes apply the interlock protocol improperly, making themselves vulnerable to MitM attacks. The analysis and experimental results show that the proposed MitM attack almost surely succeeds with very low computation overhead. We also propose countermeasures on the presented attack.

Although pre-shared key schemes are popularly used in securing communication systems, they are impractical in some applications such as ad-hoc communications. This paper proposes a new method for secret key generation between wireless endpoints. In contrast to previous studies, the present method utilizes a joint time-frequency multiscale features of wireless channels, thus yields higher bit-rate and/or larger secret key, as demonstrated in the numerical evaluations and experiments.

In modern rail transport systems, balises are widely used to exchange track-train information via air-gap interface. In this paper, we first present the vulnerabilities on the standard balise air-gap interface, and then conduct vulnerability simulations using the system parameters that were specified in the European Train Control System. The simulation results show that the vulnerabilities can be exploited to launch effective and practical attacks, which could lead to catastrophic consequences, such as train derailment or collision. To mitigate the vulnerabilities and attacks, we propose to implement a challenge-response authentication process in the air-gap interface in the existing transport infrastructure.

GSM-R is an important transport communication system to realize moving block control so as to maximize the transfer capacity. However, if the GSM-R system is compromised, the transport system has to downgrade to fixed block control which is of low capacity. This paper presents secure train-to-train communication schemes which enable moving block control even if the GSM-R system is under attack. Particularly, it elaborates two key management protocols to build secure channels between two adjacent trains. One is to setup an autonomous secure train-to-train channel with asymmetric cryptographic primitives and another is to provide secure quasi-autonomous train-to-train channel with symmetric cryptographic primitives. Both schemes are lightweight in terms of computation and communication overhead. In addition, the present scheme is compatible with the terrestrial transportation communication systems.

H.264/SVC (Scalable Video Coding) codestreams, which consist of a single base layer and multiple enhancement layers, are designed for quality, spatial, and temporal scalabilities. They can be transmitted over networks of different bandwidths and seamlessly accessed by various terminal devices. With a huge amount of video surveillance and various devices becoming an integral part of the security infrastructure, the industry is currently starting to use the SVC standard to process digital video for surveillance applications such that clients with different network bandwidth connections and display capabilities can seamlessly access various SVC surveillance (sub)codestreams. In order to guarantee the trustworthiness and integrity of received SVC codestreams, engineers and researchers have proposed several authentication schemes to protect video data. However, existing algorithms cannot simultaneously satisfy both efficiency and robustness for SVC surveillance codestreams. Hence, in this article, a highly efficient and robust authentication scheme, named TrustSSV (Trust Scalable Surveillance Video), is proposed. Based on quality/spatial scalable characteristics of SVC codestreams, TrustSSV combines cryptographic and content-based authentication techniques to authenticate the base layer and enhancement layers, respectively. Based on temporal scalable characteristics of surveillance codestreams, TrustSSV extracts, updates, and authenticates foreground features for each access unit dynamically with background model support. Using SVC test sequences, our experimental results indicate that the scheme is able to distinguish between content-preserving and content-changing manipulations and to pinpoint tampered locations. Compared with existing schemes, the proposed scheme incurs very small computation and communication costs.

Denial-of-service (DoS) and distributed DoS (DDoS) are among the major threats to cyber-security, and client puzzle, which demands a client to perform computationally expensive operations before being granted services from a server, is a well-known countermeasure to them. However, an attacker can inflate its capability of DoS/DDoS attacks with fast puzzle-solving software and/or built-in graphics processing unit (GPU) hardware to significantly weaken the effectiveness of client puzzles. In this paper, we study how to prevent DoS/DDoS attackers from inflating their puzzle-solving capabilities. To this end, we introduce a new client puzzle referred to as software puzzle. Unlike the existing client puzzle schemes, which publish their puzzle algorithms in advance, a puzzle algorithm in the present software puzzle scheme is randomly generated only after a client request is received at the server side and the algorithm is generated such that: 1) an attacker is unable to prepare an implementation to solve the puzzle in advance and 2) the attacker needs considerable effort in translating a central processing unit puzzle software to its functionally equivalent GPU version such that the translation cannot be done in real time. Moreover, we show how to implement software puzzle in the generic server-browser model.

With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) systems have become a popular way to exchange files and deliver multimedia content over the internet. However, current P2P systems are suffering from “free-riding” due to the peers' selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other in a heterogeneous network consisting of wired and wireless peers. The proposed mechanism can provide differentiated service to peers with different credits through biased resource allocation. A Stackelberg game is formulated to obtain the optimal pricing and purchasing strategies, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. In particular, peers' heterogeneity and selfish nature are taken into consideration when designing the utility functions for the Stackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.

A scalable video coding (SVC) codestream consists of one base layer and possibly several enhancement layers. The base layer, which contains the lowest quality and resolution images, is the foundation of the SVC codestream and must be delivered to recipients, whereas enhancement layers contain richer contour/texture of images in order to supplement the base layer in resolution, quality, and temporal scalabilities. This paper presents a novel hybrid authentication (HAU) scheme. The HAU employs both cryptographic authentication and content-based authentication techniques to ensure integrity and authenticity of the SVC codestreams. Our analysis and experimental results indicate that the HAU is able to detect malicious manipulations and locate the tampered image regions while is robust to content-preserving manipulations for enhancement layers. Although our focus in this paper is on authenticating H.264/SVC codestreams, the proposed technique is also applicable to authenticate other scalable multimedia contents such as MPEG-4 fine grain scalability and JPEG2000 codestreams.

Credit-based incentive mechanisms are widely adopted in today's peer-to-peer (P2P) multimedia streaming systems due to their effectiveness in fighting against “free-riding” and stimulating the cooperation between peers. In this paper, we investigate the optimal and suboptimal credits allocation strategies for peers to maximize their viewing experience in such systems. Especially, the dynamic changing feature of credits is taken into consideration when we formulate the problem, and the optimal credits allocation is shown to be a staircase-like function over time. Then, based on the special features of the optimal credits allocation strategy, an effective double-loop iterative algorithm is proposed. For the consideration of practical implementation, three low-complexity suboptimal credits allocation strategies are proposed. It is shown that each of the suboptimal strategies has its own feature and is applicable to different scenarios.

With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) streaming systems are gradually replacing their server-client-based counterparts in the realm of delivering multimedia content over the internet. However, current P2P streaming systems are suffering from “free-riding” due to the peers' selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other. The proposed mechanism provides service differentiation for peers with different credits and connection types through biased resource allocation. A Stackelberg game is formulated to obtain the optimal resource allocation strategy, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. Especially, the selfish nature of peers is taken into consideration when designing the utility functions of the Stackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.

In social networking, the users' friends are dynamically distributed all over the world and served by many nontrusted and independent providers, thus it is hard to explicitly set-up their public keys with the conventional Public Key Infrastructure so as to provide secure social networking. The present paper aims to build transparently secure channels for social networking. It employs identity-based encryption schemes to enable secure communication with legacy social networking applications based on the users' identities such as telephone numbers. In other words, by stealthily injecting the identity-based security function into the existing social networking applications, a user can securely send the messages with the (insecure) applications. The experiments on the instant communication with Android smartphones demonstrate its effectiveness and efficiency.

This paper presents a novel Multi-message Ciphertext Policy Attribute-Based Encryption (MCP-ABE) technique, and employs the MCP-ABE to design an access control scheme for sharing scalable media based on data consumers' attributes (e.g., age, nationality, or gender) rather than an explicit list of the consumers' names. The scheme is efficient and flexible because MCP-ABE allows a content provider to specify an access policy and encrypt multiple messages within one ciphertext such that only the users whose attributes satisfy the access policy can decrypt the ciphertext. Moreover, the paper shows how to support resource-limited mobile devices by offloading computational intensive operations to cloud servers while without compromising data privacy.

Public-key watermarking schemes are required to possess two desirable properties: allowing everyone to determine whether a watermark exists in an image or not and ensuring high detection probability in case of malicious modification. In this paper we propose an attack which pollutes the watermark embedded in an image with an optimal colored noise so as to fool the detector of the underlying public-key watermarking scheme. We further show how to apply the proposed pollution attack to public-key subspace watermarking schemes to generate pirated images of high quality but of low detection probability. Our experiment results demonstrate that the proposed pollution attack is very effective.

The emerging vehicular communications will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment. Due to high vehicular speed, sporadic connection, limited communication range, and large volume of data that need to be transmitted, vehicular communications have the crucial requirements of fast authentication and encryption/decryption. This paper addresses the new and special challenges related to vehicular communications, such as large overhead and latency, presents our Hybrid scheme HASVC to address the authentication issue, and evaluates its performance so as to meet the stringent requirement of real time vehicular communications. The experimental results show that our authentication protocol can securely protect the exchanged information with less overhead and less authentication latency.

This paper investigates previous blind SS schemes which do not employ the host objects in the detection process, especially the Improved Spread Spectrum (ISS) scheme. It shows that these schemes result in a high false positive probability even in the absence of any attack. Furthermore, we present an Obscure SS (OSS) scheme which balances the detector blindness and detector performance by exploiting some auxiliary data which are not harmful to the copyright. The main advantages of OSS over the previous blind SS schemes are: (1) it does not make any statistical assumption on the host object, and (2) It achieves low false probabilities (positive and negative) just as in non-blind SS schemes.

This paper proposes a full fidelity watermarking on JPEG bitstreams. That is, without a designated decoder/player, the watermarked bitstream can be decompressed to the original one. To this end, the scheme modifies the JPEG Huffman tables and the bitstream synchronically according to the watermark. Thus it is unnecessary for the JPEG decompressor to recover DCT coefficients because they are not modified at all. In addition, the present scheme is applicable to any JPEG bitstream even if its Huffman tables are optimized.

Unlike most of the spams targeting for advertisements only, phishing spams try to cheat the email recipients with bogus sender addresses so as to obtain confidential information of the recipients. This paper presents a Sender Authentication Protocol (SAP) which aims to filter out this kind of crafty spoofing emails. To this end, SAP challenges the claimed-sender with the historical emails so as to verify the authenticity of the sender. As it does not change the email protocol, and is able to be embedded into the off-the-shelf email software such as Microsoft OutlookTM, SAP is not only easy to be deployed, but also fully compatible with other anti-spam technologies. To illustrate its feasibility, we develop a SAP add-in for enhancing Microsoft OutlookTM. The SAP add-in will be started automatically as long as Outlook is started. As the enhanced OutlookTMhas the same user interface as the original one, the add-in is transparent and friendly to the users.

According to a popular pattern recognition method, this paper proposes two methods to generate a secret from individual's biometric information, such as fingerprint feature points. One method is to recover a secret based on Chinese Remainder approach. Another is to fake some chaff points based on the general biometric feature points, and then recover the secret based on polynomial fitting. In order to guarantee the integrity, a message authentic code (MAC) for all the points is stored along with the points in public. Only if a user is able to re-generate a secret that matches the stored MAC, the user will be confirmed. To keep pace with the gradual change of the measured features, the secret and the points are able to be updated adaptively.

"Vehicle to Grid" power or V2G will be a new green energy scheme that allows electricity to flow from Electric Vehicles (EVs) to power lines. The objective of this paper is to design and develop a secure wireless communication platform for V2G research, with the aim to develop a suitable wireless test bed for the V2G in Singapore. First, this paper presents our system block diagram and required methods design which include V2G architecture design, integrated OBU (on-board unit) design, base station (Aggregator), Telematics, V2G authentication protocol, and schedule optimization of EVs against Grid conditions. It then focuses on the authentication protocol for secure wireless communications between the Aggregators and EVs. It addresses the new and special challenges related to EVs, such as large overhead and latency, which are crucial for secure wireless communications with dynamic and fast moving EVs. Finally, the paper presents the initial test results and shows that our authentication protocol can securely protect the exchanged information with less overhead and less authentication latency.

High Dynamic Range (HDR) images capture the full range of luminance present in real world scenes, and unlike Low Dynamic Range (LDR) images, can simultaneously contain detailed information in the deepest of shadows and the brightest of light sources. In order to render HDR image on LDR displayers, it is often necessary to create LDR depictions of HDR images at the cost of contrast information loss. To reduce the loss, this paper enables to render HDRI (High Dynamic Range Image) with multiple low-bit images periodically. From the viewpoint of a human, the pixel value is fractural. It does not adjust the tones but can reconstruct HDR images.

Teleophthalmology (TeleOph) is an electronic counterpart of today's face-to-face, patient-to-specialist ophthalmology system. It enables one or more ophthalmologists to remotely examine a patient's condition via a confidential and authentic communication channel. Specifically, TeleOph allows a trained nonspecialist in a primary clinic to screen the patients with digital instruments (e.g., camera, ophthalmoscope). The acquired medical data are delivered to the hospital where an ophthalmologist will review the data collected and, if required, provide further consultation for the patient through a real-time secure channel established over a public Internet network. If necessary, the ophthalmologist is able to further sample the images/video of the patient's eyes remotely. In order to increase the productivity of the ophthalmologist in terms of number of patients reviewed, and to increase the efficiency of network resource, we manage the network bandwidth based on a Poisson model to estimate patient arrival at the clinics, and the rate of ophthalmologist consultation service for better overall system efficiency. The main objective of TeleOph is therefore to provide the remote patients with a cost-effective access to specialist's eye checkups at primary healthcare clinics, and at the same time, minimize unnecessary face-to-face consultation at the hospital specialist's center.

The availability of real-time CAN-ECU data and the emerging vehicular networks are critical for advanced vehicle monitoring and control. This paper presents our integrated info-security scheme, real-time hardware/software solutions and their application scenarios. The core is the Integrated Info-Security Circuit Board to communicate with ECUs and sensors inside a vehicle through CAN Bus, LIN Bus, FlexRay and MOST Bus with wire interfaces, and to communicate with other vehicles, road-side infrastructure and mobile phones with wireless interfaces. We have built the first prototype with two circuit boards which communicate directly with CAN network and process CAN messages. One circuit board is used for monitoring and control Vehicle Functions via real-time CAN messages, while another circuit board is used to monitoring and control vehicle itself to against the theft. The demonstrations in the first prototype show that our solutions work very well.

When a mobile node roams in the mobile networks, its access router and routing path keeps changing. Hence the mobile node needs to authenticate the new access router and establish a new key for secure communication. To this motivation, this paper proposes a lightweight, efficient and scalable protocol to establish and update the authentication key in the mobile IPv6 networks.

Most traitor tracing schemes in the literature assume that pirate decoders are stateless. This stateless assumption, however, is unrealistic especially in case of hardware decoders. Any tracing algorithm based on the above assumption may draw a wrong detecting conclusion. The present approach converts a tracing algorithm for stateless decoder into a tracing algorithm for stateful decoder. By employing a robust watermarking scheme, the proposed approach ensures that tracing processes and normal broadcast processes are indistinguishable for pirate decoders. This in turn allows a tracer to incriminate at least one traitor from a pirate decoder. Since the communication overhead for conversion is merely linear to the number of traitors and independent of the number of users, our approach is more efficient than the techniques in.

In a palette image file, each color is pointed by at least one palette entry and each pixel is represented with one of the color pointers. Since many palette images use a portion of palette entries only, this paper presents a distortion-free watermarking on palette images by exploiting the unused entries. It allocates x palette entries to one color such that each pixel of the color is able to embed log2x bits. In order to achieve high embedding capacity, we formulate the relationship among embedding capacity, color occurrence, and unused palette entries. By solving the formula and allocating the palette entries in a sub-optimal manner, the present scheme provides a roughly optimal capacity. Our experiment demonstrates that the present scheme is of high watermarking capacity.

A self-healing key distribution scheme enables a large group of users (sensor nodes) to establish a session key dynamically over an unreliable, or lossy wireless network. The main property of self-healing ensures that the qualified users can recover the lost session keys on their own from the broadcast packets and some private information, without any additional communication with the group manager, thus decreasing the load on the group manager. The only requirement for a user to recover the lost session keys, is its membership in the group both before and after the sessions in which the broadcast packets containing the keys are sent. Self-healing approach of key distribution is stateless in the sense that a user who has been off-line for some period is able to recover the lost session keys immediately after coming back on-line. This paper presents a new self-healing key distribution scheme with revocation capability that requires constant storage of personal keys for each user and we feel, it is more efficient than the previous schemes in terms of communication complexity. The novelty of this scheme is to use a different and more efficient self-healing mechanism compared to the ones in the literature. The scheme is supported by a proper security analysis in an appropriate security model. It is unconditionally secure and achieves both forward and backward secrecy. Moreover, unlike previous works, proposed self-healing key distribution is not restricted to m sessions in Setup phase.

Aggregating sensor reports can significantly reduce sensor network traffics, but to provide confidentiality to these reports might disable meaningful aggregation. This paper tackles the problem by proposing a resilient aggregation scheme RAS that achieves 2-level aggregation at cluster and network respectively. Cluster aggregation collects local sensing reports, then authenticates and encrypts the aggregated report. Encrypted reports, while traversing the network to base station, are further processed by network aggregators. Upon receiving an encrypted report, a network aggregator matches it with the stored keywords and then processes the report based on certain policies such as forwarding to the next hop, updating it or simply dropping it on detecting duplicates. The reports are only decrypted at their destinations. We ensure the confidentiality of the reports, as the intermediate aggregators learn nothing about them except several encrypted keywords. We analyze the resilience and overhead of the RAS scheme with the simulation results showing that the scheme is resilient against node compromise attack with reasonable performance overhead. Our analysis show that the scheme is secure, resilient and efficient.

The buyer-seller watermarking protocol enables a seller to successfully identify at least one traitor from a pirated copy, while prevent the seller from framing an innocent buyer. Kuribayashi and Tanaka proposed a fingerprinting protocol whose objectives are : (1) a buyer can obtain a watermarked copy anonymously, (2) a seller can trace the illegal distributor effectively. However, this paper points out that Kuribayashi-Tanaka fingerprinting scheme has flaws in achieving the above objectives. Furthermore, we improve Kuribayashi-Tanaka fingerprinting scheme so as to patch the security flaws.

This paper evaluates Ouda and El-Sakka scheme (OE for short) which is a tamper-localization watermarking. OE is a public block-wise scheme which enables everyone to identify the tampered regions. However, we point out OE scheme may identify a forge blocked to be intact, and accuse an intact block to be corrupted.

This paper presents a collusion attack to a fingerprinting scheme. To this end, it creates a pirated copy by adaptively separating the traitors into groups and then applying either LCCA or majority attack. Since the pirated copy discloses no watermarks of the traitors, the fingerprinting scheme fails to trace the traitors. Our experiments demonstrate that the attack is effective.

Wireless communications typically feature narrow-bandwidth, error-prone and are more vulnerable than the wired counterparts. Numerous packet based stream authentication schemes are proposed for authenticating stream transmitted over erasure wireless channel. However, by fixing the packets in transmission, any packet manipulation will cause authentication failure. In this paper, we assume a more flexible scenario where a gateway, between a producer and a receiver, is able to make adaptation operations over a stream to better fit in the wireless channel. Our adaptive scheme enables packet manipulation by committing the changes, while previous schemes are simply unapplicable. We elaborate the adaptive authentication scheme based on layered structure of a video stream as well as its encoding, packing, amortizing and verifying methods in this paper. The security is analyzed in terms of authentication probability, in which higher authentication rate (95%) is achieved with less overhead per packet. The performance analysis show that our scheme reduces more than 56% of the overhead per packet compared with that of packet based schemes.

To provide a trusted database query service, a database server returns to a client a query answer as well as a correctness proof for the answer. The trusted query result should have the following properties: (1) soundness, (2) completeness, (3) privacy. The present trust answer scheme (TrustAnswer for short) addresses a new query called as trusted distance query. Although distance query is widely used in database search, few previous schemes are applicable to trusted distance query. TrustAnswer creates a virtual database, and sets up a multi-dimensional Merkle tree for the virtual database. For each query on the original database, the server sends to the client a query result with the virtual database, as well as a proof for soundness and completeness. To demonstrate the practicality of TrustAnswer, we develop a prototype for authenticated content-aware video query on handphone. Since the prototype is light weight in computation and communication, it is suitable to be used in hand-hold devices.

JPEG 2000 is an international standard for still image compression in the 21st century. Part 8 of the standard, named JPSEC, is concerned with all the security aspects, in particular to access control and authentication. This paper presents a novel access control scheme for JPEG 2000 image code-streams. The proposed scheme is secure against collusion attacks and highly efficient. The scheme is also very flexible, allowing access control to JPEG 2000 image code-streams according to any combination of resolution, quality layer and region of interest. The "encrypt once, decrypt many ways" property of our scheme is designed to work seamlessly with the "compress once, decompress many ways" feature of the JPEG 2000 image code-streams. Our prototype implementation shows that the scheme is practical and is completely compatible with the core part of the JPEG 2000 standard.

This paper presents an authentication watermarking method which can localize the tampering regions in fine granularity. Unlike the previous block-wise tampering-localization watermarking methods which embed authentication messages including the image index, block index and block hash, the present scheme encodes the block hash with a systematic error correcting code and then merely embeds the parity symbols into the blocks. At the verification stage, each block hash can be recovered with the embedded parity symbols if the number of tampered blocks is within a threshold value. Since the total size of parity symbols is much smaller than the total size of block hashes, the present paper reduces the size of the authentication message greatly and hence increases the localizing granularity or security strength.

To announce the ownership of video such as TV programs, the owner usually embeds his logo into the programs in a visible way. Since the logo and its position are usually fixed in the video frames, an adversary can completely remove the logo from the video without video quality loss. In order to thwart this removal attack, the present paper randomly changes logo location and shape without obviously reducing the visibility and fidelity of the logo

It is an open problem of how to protect the traffics and at the same time, to support In-network processing in sensor networks. This paper tackles the problem by proposing an efficient model of categorizing encrypted data transmitted on sensor networks. An aggregator, an intermediate sensor node in our setting, is embedded with a set of searching in encrypted format. Upon receiving an encrypted message it matches the message with the keywords and then processes the message based on certain policies such as forwarding the original message to the next hop updating it and forwarding or simply dropping it on detecting duplicates. The messages are encrypted before being sent out and decrypted only at their destination. Although the intermediate classifiers can categorize the messages they learn nothing about the encrypted messages except several encrypted keywords even the statistic information. The secure and efficient aggregation SEA scheme uses Bloom filter to further reduce transmission cost. The performance analysis shows that the computational cost and communication cost are well balanced.

It is well known that packets may be lost when video stream is transmitted over wireless network. To authenticate real time multicast streams with less overhead but at higher probabilities, most of previous stream authentication schemes insert packet hashes into the packet bodies explicitly. The present scheme enables to remove the packet hashes from the packet overhead in lossy networks. It encodes the packet data with single encoding operation and only encapsulates the parity symbols into the packets overhead. Thus, it reduces the communication overhead as well as encoding time

This paper presents an adaptive collusion attack on a buyer authentication watermarking scheme. To accomplish this attack, the traitors (i.e., dishonest buyers) select the pixels of their watermarked images generated from the same original image and average the selected pixels so as to remove the watermark information. Additionally, the forged image is of higher quality than any watermarked image. Both theoretical and experimental results demonstrate that our attack is very effective.

This paper presents three scalable and efficient schemes for authenticating MPEG-4 streams: the Flat Authentication Scheme, the Progressive Authentication Scheme, and the Hierarchical Authentication Scheme. All the schemes allow authentication of MPEG-4 streams over lossy networks by integrating seamlessly digital signatures and erasure correction coding with MPEG-4's fine granular scalability. A prominent feature of our schemes is their "sign once, verify many ways" property, i.e., they generate only one digital signature per compressed MPEG-4 object group, but allow clients to verify the authenticity of any down-scaled version of the original signed object group.

MPEG-4 IPMPX (intellectual property management and protection extension) is the latest ISO standard which provides a flexible framework for protecting MPEG streams. The message mechanism of IPMPX enables interoperability among IPMPX-compliant devices no matter which protection methods are embedded. This paper highlights several problems in the message syntax of IPMPX: the tool delivery message IPMP/spl I.bar/ToolES/spl I.bar/AU is vulnerable to network attack, the authentication message IMP/spl I.bar/Mutual/spl I.bar/Authentication is incapable of defending against forgery attack, and the configuration message IPMP/spl I.bar/SelectiveDecrptionInit is ambiguous and redundant. We propose a number of remedies to those problems, which can be incorporated into a corrigenda to improve the present ISO MPEG-4 IPMP standard.

This paper presents a linear combination collusion attack (LCCA) which is a generalization of the average attack model. LCCA generates a pirated image of good quality but prevents traitors from being identified. As an application example, LCCA is used to attack a fingerprinting scheme published in IEEE Transactions on Signal Processing. The theoretical analysis and experiment results demonstrate that the attack is practical and efficient.

A complete MPEG-4 stream consists of many elementary streams, which may be generated by different authors. In the scenario of this paper, each author signs his own authentic elementary stream independently, and then an untrusted distributor aggregates these signatures into only one. Based on the unique signature, a client is able to verify the received MPEG-4 stream with the certificates of all the authors other than the certificate of the distributor. In addition, each author cannot deny what he has signed even if he is willing to admit a signature on another ES. This aggregated signature scheme is efficient in terms of transmission overhead and verification time since only one signature is processed in the client side.

Sequential aggregate signature, first introduced and formalized by A. Lysyanskaya et al. (see EUROCRYPT 2004, p.74-90, 2004), is emerging as a useful tool to ensure routing security and at the same time to improve performance. We propose a new mechanism to construct sequential aggregate signatures based on the cipher block chaining (CBC) mode, which is different from previous known results. We then construct an efficient sequential aggregate signature scheme and show that our construction is provably secure in the random oracle paradigm, assuming that the RSA problem is hard. Finally, we propose an interesting aggregate routing protocol for wireless ad hoc networks as an immediate application of our protocol.

This paper proposes a counterfeiting attack on an SVD-based ownership watermarking scheme. In the proposed attack, the adversary can claim the rightful ownership of any image by fabricating a bogus "original" image and meaningful logo. To defend against this attack, this paper proposes to generate the watermark from the original image and owner's logo with a one-way function.

This paper presents a compliant encryption method for JPEG 2000 codestreams such that the encryption process does not introduce superfluous JPEG2000 markers in the protected codestream, i.e., the protected codestream preserves the syntax of the original codestream. The proposed encryption method works with any standard ciphers, incurs no storage overhead, introduces negligible computational cost and maintains all the desirable properties of the original JPEG 2000 codestream such as error resilience and scalability.

This paper presents an authentication scheme and an access control scheme for JPEG2000 image codestreams based on hash chains. Both schemes are fully compatible with the core part of JPEG2000 standard. The authentication scheme allows users to verify the authenticity of sub-images progressively extracted from a single codestream protected with a single signature, and the access control scheme allows users to access sub-images partially decrypted from a single encrypted JPEG2000 codestream. In addition, the two schemes can be integrated to provide both authenticity and access control simultaneously. Our experiments demonstrate the two scheme are very efficient and highly scalable.

In ACM Multimedia'2002, a video proxy scheme was proposed for secure video delivery. In the scheme, a video is cached in proxies in encrypted form so that it remains non-disclosed even if the proxies are compromised. The proxies re-encrypt the video before its distribution, and different clients would receive different keys for the protected video. In this paper we present a security analysis on the scheme and show that the scheme is subject to collusion attack. Two or more clients working together can find out video server's secret keys and hence compromise the system. The countermeasure to the collusion attack is presented.

Watermarking technology was originally proposed for copyright protection. Recently it has been applied to media authentication so that a proof of authenticity is inserted into the media instead of being appended to the media as a separated attachment. However, security requirements of the authentication are overlooked in some authentication watermark schemes. In this paper we analyze three authentication watermarking schemes and point out their security flaws. The first scheme is the color authentication scheme in [S.C. Byun et al., 2002]. The scheme is not secure in the sense that as long as an attacker obtains one authenticated image, he is able to forge authentic images without the secret key. The second scheme [Ping Wah Wing, et al., 2001] is an authentication scheme but it is extended for ownership incorrectly. The third one, the robust invertible watermarking scheme [J. Friedrich et al., 2002], employs a multiple of secret random sequences to produce a watermark. However these sequences are independent of the original images, i.e., they remain invariable for different images. An adversary, having sufficient number of original images, can reconstruct the secret sequences by solving simultaneous equations. With these reconstructed sequences, the attacker can forge authentic image freely. The attack can be thwarted with content related sequences generated from both the secret key and the original image.

JPEG2000 is an emerging standard for still image compression and is becoming the solution of choice for many digital imaging fields and applications. An important aspect of JPEG2000 is its "compress once, decompress many ways" property [1], i. e., it allows extraction of various sub-images (e.g., images with various resolutions, pixel fidelities, tiles and components) all from a single compressed image codestream. In this paper, we present a flexible and scalable authentication scheme for JPEG2000 images based on the Merkle hash tree and digital signature. Our scheme is fully compatible with JPEG2000 and possesses a "sign once, verify many ways" property. That is, it allows users to verify the authenticity and integrity of different sub-images extracted from a single compressed codestream protected with a single digital signature.