We present Mask, an efficient and tunable algorithm for hiding volume patterns in multi-maps. Volume-pattern leakage, referring to the observable size of data returned by a query, enables adversaries to infer sensitive dataset information, posing severe privacy risks in multi-map scenarios. Designed to address this issue, Mask focuses on balancing storage/query overhead and privacy (a key trade-off in this field), allowing users to define parameters for random data distribution across buckets to realize fine-grained control over storage and query performance, integrating Bloom filters with a bounded cache for efficient indexing (optimizing performance under skewed query workloads); extended to MaskIO for reduced client-side storage, it obfuscates query indexes and uploads them to the server, achieving constant-bounded client storage overhead, and experiments show Mask outperforms the bucket-based peer Veil with 2–4 × higher performance and a lower stash ratio.

The massive growth of data has brought vigorous vitality to Internet-of-Things (IoT). It has also brought new challenges, such as confidentiality privacy protection and redundant data transmission. Concerning this regard, data aggregation serves as an efficient technique to minimize the transmission frequency among massive objects in smart grid(SG). By aggregating a large amount of the same type of data while satisfying the protection of user privacy. With the advent of the post-quantum era, a good aggregation scheme must provide quantum resistance while ensuring the secure aggregation of ciphertext power data. However, the excessive overhead limits anti-quantum algorithms from being widely used in SG where resource devices are limited. Therefore, it is an important part of the current private data security aggregation technology to find a low cost and lightweight inverse quantum algorithm to achieve user data security aggregation. In this paper, we propose an improved NTRU-based cryptosystem with multidimensional coding, referred to as multidimensional coding NTRU (MC-NTRU). and use the lattice batch signature technique, which improves the efficiency of the scheme while satisfying the anti-quantum attack. Based on these, we design the multidimensional auditable lattice-based privacy-preserving data aggregation scheme(MA-PPDA) for privacy data on resource-limited IoT devices such as smart grids. In addition to this, the scheme achieves fault tolerance of the scheme by adding zeros and random numbers to the user data. The comparative study against existing approaches demonstrates that the proposed scheme not only adheres to critical security aspects including user privacy, data confidentiality, integrity, and authenticity, but also decreases both communication and computational burdens on the system. This makes our scheme particularly apt for IoT environments characterized by constrained device resources.

Federated Learning (FL) enables collaborative model training while preserving data privacy, making it particularly attractive for large-scale Internet of Things (IoT) systems. However, in practical deployments, data collected by distributed clients are often non-independent and identically distributed (Non-IID), which amplifies the vulnerability of FL to poisoning attacks. Among them, Label-Flipping Attacks (LFA) are especially stealthy, as they can induce targeted misclassification without noticeably affecting overall accuracy, posing serious risks to safety-critical IoT applications. In this paper, we propose NeuDFL, a lightweight and robust defense framework against LFA under Non-IID settings. Unlike many existing defenses that primarily rely on gradient analysis over the full model update space or auxiliary clean datasets, NeuDFL exploits lightweight class-wise parameter statistics extracted from the final fully connected layer. By leveraging the cumulative and task-aligned nature of model parameters, NeuDFL enables reliable identification of attacked classes and filters malicious clients via an adaptive statistical threshold, improving robustness to data heterogeneity while incurring low computational overhead. Extensive experiments on multiple datasets demonstrate that NeuDFL offers an effective and efficient defense against label-flipping attacks, providing a robust solution for federated learning in complex real-world environments.

Vertical Federated Learning (VFL) is a distributed machine learning paradigm designed for scenarios with vertically partitioned data features, making it highly compatible with Internet of Things (IoT) ecosystems. While promoting collaborative modeling among IoT devices, VFL also introduces new security risks, particularly backdoor attacks. Existing VFL backdoor attacks typically establish associations between triggers and target labels during the training phase by manipulating intermediate model outputs, making them easily detectable by advanced defense mechanisms. This paper proposes Hijacking-based Backdoor Attack (HBA), which for the first time innovatively achieves backdoor attack by exchanging the forward embeddings during VFL prediction phase, without embedding traditional triggers. HBA leverages intrinsic semantic relationships in the embedding space to hijack the decision-making process of the top model during inference. HBA’s effectiveness depends on the discriminative nature of the features extracted by the bottom model, and since it does not alter the training process, it can evade most defense mechanisms based on training behavior monitoring. Experiments demonstrate that HBA achieves an attack success rate of 99.9% in classification tasks without compromising the original task’s accuracy. Furthermore, existing defense mechanisms struggle to effectively counter HBA without degrading the model’s original task performance.

Intelligent vehicle collaborative sensing faces critical challenges in dynamic spatio-temporal modeling and resource optimization. Existing approaches suffer from static topological assumptions and lack principled uncertainty quantification for sensing decisions. To address this, we present temporal-implicit topology attention network (TITAN)-strategic multicriteria active resource targeting (SMART), a unified framework integrating the TITAN with SMART. TITAN incorporates three innovations: implicit topology relation learner (ITRL), dynamic attention neighborhood aggregator (DANA), and temporal propagation attention module (TPAM). Importantly, TITAN is a spatio-temporal attention network, where ITRL and DANA operate on implicit and explicit spatial relationships, while TPAM captures multiscale temporal propagation dynamics. Experimental validation demonstrates that TITAN–SMART achieves target accuracy with up to 46.2% less resource cost than state-of-the-art baselines, maintaining scalability across large-scale networks. By explicitly targeting the gap between passive prediction on fully observed graphs and active sensing under strict sensing-resource constraints, TITAN–SMART enables principled selection of where to sense next rather than only forecasting future states. The framework’s interpretable decision patterns establish it as a practical solution for industrial Internet of Things (IoT) deployments.

As electric vehicle (EV) utilization in energy trading expands, protecting user identity privacy during transactions has become a critical area of research. Current privacy protection schemes face significant challenges, including the risks of identity leakage, insufficient protection against double signatures, and low verification efficiency. To address these challenges, this document proposes a lightweight identity privacy protection scheme based on blockchain technology from the consortium. The scheme introduces an improved linkable ring signature algorithm, which guarantees identity anonymity while effectively preventing double signature attacks. In addition, this paper develops a batch aggregation signature and verification algorithm designed to significantly improve verification efficiency in highly concurrent environments. Theoretical analysis and simulation experiments demonstrate that the proposed scheme outperforms existing solutions in security, computational efficiency, and communication overhead. Compared to existing solutions, this approach provides substantial improvements, making it a promising approach to secure user privacy within the rapidly evolving domain of EV energy trading.

Federated Learning is a widely used distributed machine learning framework that allows clients to collaboratively train a global model by uploading local gradients while keeping data stored locally, thus protecting user privacy. However, attackers can still infer local data from gradients. Recently, integrating differential privacy into FL has become a popular approach to ensure strong privacy guarantees. This paper proposes a Decentralized Federated Learning Framework with Enhanced Privacy and Optimized Fairness (DFL-EPOF). First, noise is added to local parameters before uploading, and a local differential privacy mechanism ensures data privacy. An adaptive privacy budget allocation strategy, based on data sensitivity, dynamically controls noise levels to balance privacy protection and model accuracy. Second, a weighted aggregation method based on clients' data volume, trustworthiness, and participation frequency is used to optimize fairness, ensuring balanced contributions. Finally, a decentralized blockchain-based architecture is implemented to enhance transparency and immutability, ensuring reliable model updates and data transmission. Experimental results show that DFL-EPOF improves privacy protection, fairness, and system robustness, balancing privacy and accuracy effectively.

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. Cross-chain identity authentication is the primary pre-requisite of cross-chain security interaction. However, existing cross-chain identity authentication protocol generally has issues such as insufficient decentralization, poor universality, and low authentication efficiency. These issues compromise the reliability of cross-chain interactions. Based on group signature and zero-knowledge proof, this paper proposes a cross-chain identity authentication protocol to address these concerns. The protocol employs Decentralized Identifiers(DIDs) as the global identity identifier of users, utilizes zero-knowledge proof to provide privacy for the verification of users' identities when joining the group, and then constructs the users' transaction signatures through group signatures to hide the users' identities. This approach not only reduces the risk of users' privacy leakage but also facilitates mutual recognition of identities among cross-chain systems. Finally, the security analysis and experimental analysis shows the correctness and efficiency of the proposed scheme.

With the rapid development of the Internet of Vehicles (IoV),Nevertheless, Federated Learning (FL) plays an increasingly crucial role in the secure data sharing within this field. Nevertheless, data in IoV settings frequently display Non-IID traits, which exerts a considerable influence on the efficiency and performance of FL. To enhance the model convergence speed and global performance under conditions of data imbalance and high heterogeneity, this paper presents a FL algorithm, named FedAE, that dynamically adjusts the local training epochs based on the changes in the AUC of model. FedAE assigns distinct local training epochs based on the quantity of training data in the initial stage and adaptively modifies them in subsequent stages in accordance with the dynamic changes in the AUC of both the global model and each client's model. Based on the performance disparity of the local training equipment, the algorithm establishes diverse training iterations to enhance the utilization of the local equipment and the training efficiency of the algorithm. Experimental results show that, compared to FedProx, the proposed approach significantly enhances model convergence efficiency and generalization ability. Specifically, FedAE reduces the loss by 26 %, demonstrating greater robustness, especially in scenarios with imbalanced data distributions.

As the core application of blockchain technology, smart contracts have been widely used in many fields such as finance, supply chain, and copyright management. Smart contracts are prone to various vulnerabilities that attackers can exploit to steal or freeze funds. Traditional vulnerability detection methods rely heavily on complex rules defined by experts, which are difficult to adapt to the explosion of smart contracts. Some recent studies of neural network-based vulnerability detection methods rely on contract source code, and the accuracy of bytecode-level vulnerability detection methods is low. To overcome the limitations of existing methods, we propose CV-IRTL, a new method for smart contract vulnerability detection. Specifically, CV-IRTL designs a vulnerability detection framework for smart contracts based on inverted residual network architecture and transfer learning. In particular, CV-IRTL enables vulnerability detection at the bytecode level, simplifies data preprocessing, utilizes transfer learning to better capture vulnerability characteristics and effectively address dataset imbalances. We have extensively tested CV-IRTL on a dataset containing six vulnerabilities. The experimental results show that the macro average F1-score is 90.75%, and the overall false positive rate is 9.6%, which is better than representative methods in performance.

The open architecture and inherent flexibility of the Android platform make it a prime target for malicious code penetration, posing huge security risks to users. Current graph-based detection methods have two limitations: high computational overhead and reliance on single-dimensional feature representation, which leads to poor detection performance. To address these challenges, this paper proposes a malicious code detection framework based on impact-sensitive subgraphs (LRS-GGCN), which integrates multi-level feature fusion and graph pruning techniques to improve efficiency and accuracy. First, the impact-sensitive subgraph is constructed using the LeaderRank algorithm and sensitive APIs to improve detection efficiency. Second, node-level features are enriched by fusing opcode structure and API semantic embedding to capture the syntactic and contextual properties of malicious code. At the edge level, API call frequency is combined to model interprocedural interactions and enhance the representation capability of the graph. Finally, a gated graph convolutional network (GGCN) synthesizes these heterogeneous features to achieve efficient and accurate malicious code detection. Experiments show that our method achieves an accuracy of 99.28%. In addition, the training time is reduced by about 90% compared to the unpruned baseline.

Secure neural network inference provides privacy guarantees for both the client and the server, and is an integral approach in Machine Learning as a Service Setting (MLaaS). However, the multilayer structure in the neural network introduces frequent activation function calculations, which causes large overhead. Most of the prior secure inference systems focused on designing cryptographic protocols to improve computational efficiency, but high computing and communication overhead are still bottlenecks in practicality. In this work, we refocus on the potential of shallow neural networks and propose a model with only one trainable layer to reduce the required computation. Our main contributions are in three-fold: 1) introduce training-free weights and formally prove their contribution in the model expressivity; 2) design the Self Enhanced Module that is more suitable for shallow models as an alternative for the activation function; and 3) propose a linear layer with multiscale and normalization property, named Nested & Norm Conv. We conduct extensive experiments on visual datasets and the results demonstrate the proposed single-layer trainable model holds promise as a viable platform for secure inference in practical applications.

HPC-cloud is becoming popular as it allows supercomputers to provide computing service with parallelism and high performance based on public cloud technology. Moreover, supercomputers across organizations are forming a network to scale the computational and storage capability of their service. However, the distributed computing process in the supercomputer internet requires a large amount of data transmission and access, arousing privacy and control problems. In this paper, we propose HPCBL, a blockchain-enabled decentralized data computing architecture that ensures private data sharing and computing in the Supercomputer Internet. We also propose a decentralized authentication scheme that entitles users the full control of their anonymous identity to support user private interactions with the Supercomputing Internet. The scheme supports anonymous self-derivative credentials for pair-wised access control and user-optional accountability without a trusted arbiter. We implemented a HPCBL prototype to empirically assess its performance.

The e-vote is regarded as a way to express the opinion that the voters ask for. Actually, the e-vote could be applied wildly like questionnaire, survey and feedback. Moreover, the coexistences of efficiency and security as well as transparency and privacy could be considered as building blocks in the e-vote system. The blockchain could provide a public access board to reduce the storage costs for the field consisted of the vote group manager (GM) with its vote assistants (VA). Particularly the$k$-times anonymous authentication ($k$-TAA) could also be a practical approach to preserve voters’ privacy and reduce the computation costs during the vote process. However, the e-vote scheme with pure$k$-TAA strategy could damage either the supervision of voting or the efficiency and consistency of authentication process. What’s more, the impacts of dishonest voters couldn’t be stopped until the vote end. To tackle these problems, we apply the accumulator technology to add or revoke the voters at any time and extend the framework of$k$-TAA with the update process for the e-vote on blockchain for supervision ($ k$-TEVS). In our scheme, the voter updates his membership witness and proves the fact that he is still a valid member with respective VA under the latest accumulator value. What’s more, this witness update operation is not contained in the authentication process, which means that the authentication process is still constant and efficient. Moreover, our add or delete update process with signature of knowledge needs only one pairing operation. For the security, we prove that the relaxed anonymity still holds in the$ k$-TEVS framework. Finally, We implement$ k$-TEVS scheme, the Emura’s work [1] and the Huang’s work [2] for comparison. Then we make time cost and communication cost experiments, which present the feasibility and practicality of this scheme.

With the development of the Industrial Internet of Things (IIoT), data sharing provides an important driving force for the innovative development of industry. By analysis and mining of massive data, enterprises can find new market opportunities and develop more competitive products and services. At the same time, frequent data breaches show that data faces serious security challenges in IIoT. Therefore, the security and privacy of data in IIoT must be ensured by means of identity authentication and access control technology. Traditional identity authentication methods usually only consider one-way authentication and have inherent security deficiencies that are insufficient to meet the needs of current IIoT systems. This paper proposes a blockchain-based mutual authentication scheme that replaces the traditional third-party intermediary with blockchain technology, to enhance the transparency and credibility of the identity authentication process. Moreover, the scheme combines attribute-based encryption with linkable ring signature to achieve both the protection of user identity privacy and identity tracking. Experimental results indicate that the proposed scheme demonstrates good scalability and usability.

While sharding technology helps solve performance bottlenecks in traditional blockchain networks, it also introduces new challenges.Random node allocation may lead to uneven distribution of malicious nodes, causing performance differences and security risks.Existing reputation-based sharding schemes often overlook node performance characteristics and fail to address security concerns related to leader election.In addition, full sharding reorganisation drastically reduces the performance of the entire blockchain, both in terms of overhead and time.In this paper, we propose a reputationbased sharding dynamic reorganisation network sharding scheme, which integrates node performance and behavioural characteristics into the reputation computation, and at the same time adds alternative leaders to maintain the stability of the sharding system.Based on this, through the partial reorganisation algorithm of the sharding, the nodes are reasonably allocated to balance the computing power of each shard, effectively stimulating the liveness of the sharding system and improving overall safety and performance.Simulation results show that this sharding scheme effectively reduces the consensus failure probability, solves the collusion attack problem caused by the aggregation of malicious nodes within shards, and reduces the latency of the blockchain system.This provides strong support for the reliability and performance of the blockchain sharding system.

With the rapid development of blockchain technology, the use of decentralized applications (DApps) has experienced significant growth. However, current testing methods for DApps primarily focus on testing the smart contracts on the blockchain, which are the foundation of DApps, but lack a comprehensive approach to effectively detect off-chain abnormal states. To address this issue, this paper proposes a generic abnormal state detection model based on off-chain transaction data. The model leverages the DApp program code logic to set up test oracles to analyze off-chain transaction data. Experimental results demonstrate that the model achieves high accuracy in detecting off-chain abnormal states, with a prediction accuracy as high as 96.3%. Furthermore, a comparison with other related vulnerability detection methods shows the advantages of the proposed approach.

The mobile healthcare (mHealth) is a promising and fascinating paradigm, which can dramatically improve the quality of healthcare delivery by providing remote diagnosis and medical record sharing. Now, the mHealth faces serious challenges such as data leakage and unauthorised access currently. Attribute-based encryption (ABE) which has been employed for mHealth is an excellent cryptographic primitive of securing data sharing. However, there are still some security and efficiency issues in the ABE-based data sharing scheme for mHealth. Firstly, the explicit storage of access policy may expose the privacy of users. Secondly, the computation cost is high, especially in the mHealth with IoT devices. Thirdly, the authentication of access rights to shared data is usually performed by the centralised third parties or IoT devices with limited resources. To handle the above issues, this paper presents a privacy-preserving and efficient data sharing scheme. The scheme partially hides access policy to protect user's privacy, and introduces an offline mechanism in key generation and encryption phase to improve efficiency of mHealth. Furthermore, it also provides decentralised and trusted authentication of data access right based on blockchain. The security proofs and the experiment results demonstrate that the presented scheme has better security and efficiency.

The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this article, we propose a new auditable redactable blockchain (RB) scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the RB and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.

With the rapid development of cloud computing, big data, and mobile Internet technology, data sharing across different data centers is increasingly urgent. Meanwhile, data security risks such as unauthorized access and data abuse bring serious challenges to data security sharing. Due to its poor scalability, traditional access control technology cannot play its role in the distributed computing paradigms with massive data and dynamic network environment. Attribute-based access control (ABAC) model is a potential candidate for data sharing cross data centers. But it still faces some challenges, such as different attribute semantics among data centers, and slow query speed of attributes and policies. This paper proposes an architecture for access control across data centers by constructing a global attribute set (GAS) on the blockchain to manage attributes and policies. To achieve a high efficiency, the scheme utilizes world state rather than block for query of data attributes and policies, and employs trust degree to pre authorization. Security analysis and experimental results show that the model is secure and practicable.

In a highly evolved big data era, intelligent data analysis can improve social operation efficiency and save resources. However, it also brings masses of conflicts, such as malicious mining and abuse of personal privacy information. This paper introduces a privacy protection scheme for social applications. In this scheme, attribute based searchable encryption is used to defend the security of confidential data and ensure the availability of data. Moreover, the access control structure of ciphertext strategy can meet the needs of data sharing in social applications. Security analysis shows that the scheme does not disclose privacy information in index ciphertext, search trapdoor, and equality test. Compared with plaintext information upload and sharing, the additional performance overhead caused by the scheme is acceptable. The scheme can be actually deployed in social applications.

Internet of Vehicles (IoV) being a subdivided application of the Internet of Things, is considered as one of the most prominent and emerging technologies for model transportation systems. However, security and privacy remain two key requirements for IoV networks, as communications between vehicles and other Internet-connected things are generally carried out over public channels. Some of the most typical attack issues for the IoV networks include hardware tampering, unauthorized data access, message modification, tracking vehicle locations, etc. Although there have been a number of solutions (e.g., mutual authentication and key agreement protocols) proposed to ensure the secure communication for IoV, most of them still suffer from some vulnerabilities, such as linkability, server spoofing, and replay attacks, in violation of the security requirements of IoV. Hence, it remains challenging to design secure and efficient solutions. In this article, we first take a recently proposed authentication protocol as an example and analyze the weaknesses of it with simple mathematical analysis. We then propose an improved multiserver-based authentication and key agreement protocol for IoV (called SeMAV), which applies the password and smart card to hide the private keys. We also present both formal and informal security proofs to confirm the robustness against those commonly known attacks. The theoretical comparative summary and simulation results also show that SeMAV can achieve a good performance when compared with some other related protocols in the literature.

There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.

Blockchain, which has a distributed structure, has been widely used in many areas. Especially in the area of smart cities, blockchain technology shows great potential. The security issues of blockchain affect the construction of smart cities to varying degrees. With the rapid development of quantum computation, elliptic curves cryptosystems used in blockchain are not secure enough. This paper presents a blockchain system based on lattice cipher, which can resist the attack of quantum computation. The most challenge is that the size of public keys and signatures used by lattice cryptosystems is typically very large. As a result, each block in a blockchain can only accommodate a small number of transactions. It will affect the running speed and performance of the blockchain. For overcoming this problem, we proposed a way that we only put the hash values of public keys and signatures on the blockchain and store the complete content of them on an IPFS (interplanetary file system). In this way, the number of bytes occupied by each transaction is greatly reduced. We design a bitcoin exchange scheme to evaluate the performance of the proposed quantum-resistant blockchain system. The simulation platform is verified to be available and effective.

The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples.

Certificateless aggregate signature (CLAS) schemes reduce the trust on the key generation center of identity-based signatures and thus partially address the inherent key escrow issue in identity-based cryptosystems while retaining the advantage of implementation efficiency. In the past few years, a number of new CLAS schemes were proposed to overcome the communicational and computational limitations of sensors and attain integrality, validity, and availability of patients' medical data in healthcare wireless medical sensor networks (HWMSNs). However, many of these schemes do not provide enough security guarantees. In this article, we first review a most recent CLAS scheme for HWMSNs and show that it is insecure for medical applications by presenting attacks due to a type I adversary and a type II adversary. Then, we put forth an improved construction which is provably secure under the CLAS security model in the random oracle model. Our detailed analyses demonstrate that the proposed scheme not only overcomes the security flaws but also has higher implementation efficiency and lower communication cost.

Personal privacy disclosure is one of the most serious challenges in smart industrial applications. Anonymous authentication is an effective solution to protect personal privacy. However, the existing anonymous credential protocols are not perfectly suitablefor smart industrial environments such as smart vehicles in the sense that the credential revocation issue is not well-solved. In this article, we propose a Blockchain-based Anonymous authentication with Selective revocation for Smart industrial applications (BASS) for smart industrial applications supporting attribute privacy, selective revocation, credential soundness, and multishowing-unlinkability. Specifically, an efficient selective revocation mechanism is proposed based on dynamic accumulators and the signature algorithm due to Pointcheval and Sanders as the overlay of the BASS. According to the diverse demands of credential authorities, BASS can selectively provide revocation of credentials or revocation of users. We extend BASS from single-attribute privacy to multiattribute privacy as well. Finally, we implement a prototype to evaluate the cryptographic core primitives of BASS by deploying smart contracts in Ethereum to demonstrate the validity of BASS in smart industrial applications.

Efficient security patch distribution is of essential importance for updating anti-virus software to ensure effective and timely virus detection and cleanup. In this paper, we propose a mixed strategy of patch distribution to combine the advantages of the traditional centralized patch distribution strategy and decentralized patch distribution strategy. A novel network model that contains a central node and a multiplex network composed of patch dissemination network layer and virus propagation network layer is presented, and a competing spreading dynamical process on top of the network model that simulates the interplay between virus propagation and patch dissemination is developed. Such a new framework helps in effectively analyzing the impacts of patches distribution on virus propagation, and developing more realizable schemes for restraining virus propagation. Furthermore, considering the constraints of the capacity of the central node and the bandwidth of network links, an optimal allocation approach of patches is proposed, which could simultaneously optimize multiple dynamical parameters to effectively restrain the virus propagation with a given budget.

Most existing virtual machine introspection (VMI) technologies analyze the status of a target virtual machine under the assumption that the operating system (OS) version and kernel structure information are known at the hypervisor level. In this paper, we propose a model of virtual machine (VM) security monitoring based on memory introspection. Using a hardware-based approach to acquire the physical memory of the host machine in real time, the security of the host machine and VM can be diagnosed. Furthermore, a novel approach for VM memory forensics based on the virtual machine control structure (VMCS) is put forward. By analyzing the memory of the host machine, the running VMs can be detected and their high-level semantic information can be reconstructed. Then, malicious activity in the VMs can be identified in a timely manner. Moreover, by mutually analyzing the memory content of the host machine and VMs, VM escape may be detected. Compared with previous memory introspection technologies, our solution can automatically reconstruct the comprehensive running state of a target VM without any prior knowledge and is strongly resistant to attacks with high reliability. We developed a prototype system called the VEDefender. Experimental results indicate that our system can handle the VMs of mainstream Linux and Windows OS versions with high efficiency and does not influence the performance of the host machine and VMs.

Identifying the physical person behind an SNS account has become a critical issue in investigations of SNS-involved crime cases. It is a challenging task because information provided by users on an SNS platform could be false, conflicting, missing and deceptive. One way to gain an accurate profile of a user is to link up all their multiple accounts created on different social platforms, which is referred to as Account Linkage (AL). However, existing AL techniques suffer from the problem of information unreliability. Recent advances in location acquisition and wireless communication technologies give rise to new opportunities for AL. In this paper, we propose a framework that links up multiple accounts belonging to the same individual by comparing habit patterns extracted from user-generated location data. We built a topic model to capture users habit patterns in both spatial and temporal dimensions. Results of experiments carried out on a real-world dataset demonstrate the feasibility and validity of the proposed framework.

Online identities (OIDs) refer to accounts that Internet users create on Web platforms. In investigations of OID-involved criminal cases, investigators often encounter multiple OIDs used by the same criminal among a number of suspect OIDs. Recognizing such OIDs is referred to as OID linkage (OL) and is a critical task, because it helps investigators consolidate information from different sources, identify new investigation leads, perform further analysis by connecting seemingly unrelated cases, and finally ideate the real criminal. However, few existing OL techniques can achieve satisfactory performance in criminal investigation scenarios due to information asymmetry, information unreliability, and a lack of training data. We propose an unsupervised spectral co-clustering-based OL framework that takes OID access trajectories as linkage evidence. By leveraging a spectral co-clustering algorithm, we integrate access location consistency and access time consistency as heuristics to direct the OL process. Experiments performed using actual investigation data demonstrate the feasibility and promise of the proposed framework.

Since cyber offenders often create multiple accounts on different Social Media Platforms (SMPs) to serve their disparate malicious intentions, law enforcement agency investigators often encounter the task of recognizing all the accounts used by the same person across SMPs, i.e., account linkage (AL). Although a number of techniques have been proposed for AL, their performance may be degraded by factors such as information asymmetry, poor data quality, data unavailability, as well as application scope limitation. In this paper, we solve AL in an unsupervised manner by utilizing user-generated geo-location data in SMPs, which is more robust than common clues used in existing techniques. A co-clustering-based AL framework is proposed in which account clusterings in temporal and spatial dimensions are carried out synchronously and enhance the results of each other. Experiments carried out on a real-world dataset demonstrate the feasibility and validity of the proposed framework.

Memory forensics plays an important role in security and forensic investigations. Hence, numerous studies have investigated Windows memory forensics, and considerable progress has been made. In contrast, research on Linux memory forensics is relatively sparse, and the current knowledge does not meet the requirements of forensic investigators. Existing solutions are not especially sophisticated, and their complicated operation and limited treatment range are unsatisfactory. This paper describes an adaptive approach for Linux memory analysis that can automatically identify the kernel version and recovery symbol information from an image. In particular, given a memory image or a memory snapshot without any additional information, the proposed technique can automatically reconstruct the kernel code, identify the kernel version, recover symbol table files, and extract live system information. Experimental results indicate that our method runs satisfactorily across a wide range of operating system versions.

Sparse representation based classification (SRC) could not well classify the sample belonging to different classes distribute on the same direction. To solve the problem, a Volterra kernel sparse representation based classification (Volterra-SRC) algorithm is proposed in this paper. Firstly, the original face images are divided into non overlapped patches and then mapped into a high dimensional space by utilizing the Volterra kernels. During the training stage, following by the Fisher criteria, the objective function is defined to obtain the optimal Volterra kernels via maximizing inter-class distances and minimizing intra-class distances simultaneously. During the testing stage, a voting procedure is introduced in conjunction with a sparse representation based classification to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in a face are used to determine the overall recognition outcome for the given face image. We demonstrate the experiments on ORL and Extended Yale B benchmark face databases and show that our proposed Volterra-SRC algorithm consistently outperforms the original SRC and the proposed has some advantages and robustness in case of small train number samples.

The increased use of social networking application on iPhone and iPad make these devices a goldmine for forensic investigators. Besides, QQ, Wechat, Sina Weibo and skype applications are very popular in China and didn’t draw attention to researchers. These social networking applications are used not only on computers, but also mobile phones and tablets. This paper focuses on conducting forensic analysis on these four social networking applications on iPhone and iPad devices. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application and correlation analysis with other activities. Advices to the forensic investigators are also given. It could help the investigators to describe the crime behavior and reconstruct the crime venue.

This paper briefly introduces the challenges facing collection of volatile data in a target computer. Reasons to favor physical memory analysis are also given. After describing the related work of the memory analysis, details of a windows memory analysing method are given through which it is possible to extract useful information, such as running processes , current network connections, file contents, etc., from a memory image. The method is based on a data structure in Windows known as kernel processor control region, or KPCR. Besides, details of address translation from virtual address to physical address are thoroughly discussed and an algorithm of address translation for practice is given. This method is verified on Windows XP SP2, Windows 2003 Server SP2 and Windows Vista Home Basic.