High-dimensional tensors are a cornerstone of context-aware recommendation systems. However, deploying tensor-based models within a federated learning framework faces formidable hurdles in computational efficiency, privacy preservation, and system robustness. These challenges are exacerbated by the untrusted server, which could infer sensitive user data or corrupt the global model. To systematically address these obstacles, we design and implement CSV-FTL, a Cloud-Edge Synergistic Secure and Verifiable Federated Tensor Learning framework. Our framework’s core innovations are fourfold: (1) a natively designed federated tensor model optimized for high efficiency in distributed environments; (2) a lossless double-masking mechanism that achieves strong privacy without compromising model accuracy; (3) a reconstruction algorithm for user dropout tolerance; and (4) a homomorphic hashing scheme enabling users to verify the integrity of server-side aggregation. Consequently, CSV-FTL strikes a superior balance between recommendation accuracy, computational cost, and security guarantees. To the best of our knowledge, this is the first work to seamlessly integrate a native federated tensor model with lossless privacy, dropout tolerance, and verifiable aggregation within a single, unified framework. Both rigorous theoretical analysis and extensive experiments on four real-world recommendation-related datasets validate the provable security and superior performance of our framework over state-of-the-art methods.

The rapid development and spread of digital technology, information and communications technology, have led to an increasing number of individuals and organizations relying on the Internet for their daily work and life. However, a variety of emerging threats pose significant obstacles to traditional defense strategies. Network Security Situation Assessment (NSSA) is an effective measure to protect network systems from malicious attacks and provides an effective solution to protect network security. However, the existing NSSA schemes suffer from low accuracy and poor efficiency when dealing with network traffic data, which is characterized by large scale, nonlinearity, irregularity, high dimensionality, and temporal correlation. To solve these problems, we propose a novel integration of Time-Attention and residual connections, and design a NSSA scheme for network in this paper. Specifically, we first design a Time-Attention mechanism, which can achieve sufficient feature extraction with linear complexity. Subsequently, we integrate the Time-Attention and residual connection to improve the Gated Recurrent Unit (GRU) and design a novel integration of Time-Attention and residual connections neural network model called Time Attention Gated Network (TAG-Net). TAG-Net uses Time-Attention and residual connection as a reset gate and an update gate to reduce conflicts between reset and update gates. Meanwhile, we propose a TAG-Net-based NSSA scheme for network, which can improve the assessment accuracy and efficiency. Finally, we implement our proposed scheme and provide a performance evaluation. The experimental results show an accuracy of 81.87% for the NSL-KDD dataset, 98.28% for the UNSW-NB15 dataset, and 99.99% for the Bot-IoT dataset compared to the state-of-the-art models.

The Industrial Control Network (ICN), which is characterized by real-time responsiveness and reliability, plays a key role in increasing production speed, ensuring efficient processing, and managing industrial processes. Despite tremendous advantages, ICN inevitably struggles with some challenges, such as malicious user intrusion and hacker attacks. To detect malicious intrusions in ICN, Intrusion Detection Systems (IDS) have been deployed. However, network traffic in ICN often exhibits significant temporal periodicity, and computational resources are limited on edge nodes and infrastructure gateway devices. These characteristics pose significant challenges to the design and performance of IDS. To properly solve these problems, we design a new intrusion detection method for ICN. Specifically, we first design a novel neural network model called Associative Recurrent Network (ARN), which can properly handle the relationship between previous hidden state and current input. Then, we construct a novel intrusion detection method based on the ARN, which avoids gating conflicts in traditional Recurrent Neural Network (RNN), effectively captures the temporal characteristics of ICN traffic, and maintains slightly higher computational overhead than GRU, thus demonstrating good adaptability to industrial control networks. Subsequently, through theoretical analysis of computational complexity, we demonstrate that the proposed method achieves high computational efficiency, comparable to mainstream RNN methods and superior to Transformer methods. Finally, we implement a prototype system to evaluate detection accuracy. Experimental results show that our method achieves state-of-the-art performance on the industrial control systems datasets (ICS-ADD and SWaT) and the conventional network dataset (UNSW-NB15), with average accuracies of 98.93%, 95.57%, and 98.27%, respectively.

The rise of multivariate time series (MTS) data has made prediction crucial, with deep learning models dominant yet vulnerable to adversarial attacks. These attacks use small perturbations on inputs to cause mispredictions. MTS data's inherent complexity and sensitivity demand stringent perturbation handling. To address this, we propose TFCA, an adversarial attack method focusing on feature dimension impact. TFCA calculates each feature's gradient contribution to predictions independently, establishing a feature importance ranking. It enhances temporal characteristics by integrating multi-dimensional elements (e.g., volatility, cosine direction) to constrain adversarial sample realism. Guided by this ranking, TFCA targets specific attack ratios on subsets of the time series, ensuring attack effectiveness while reducing perturbation size and improving stealthiness. Experiments on real MTS datasets against models (TCN, LSTNet, CNN) demonstrate TFCA's effectiveness, stealthiness, applicability, and transferability. The integration of post hoc explainable AI algorithms also provides interpretability. This research offers insights for enhancing MTS model robustness and security in practice.

Thanks to the advent and rapid development of fog computing, the smart grid has made great progress. In fog-based smart grid, the fog node can maintain and handle the data for the smart meter which is resource-constraint, thus improving the performance. However, the security of the data (especially for malicious data reservation) is the primary concern of the smart meter. To resist the malicious outsourced data reservation in fog-based smart grid, we propose a provable deletion method in this article. In our method, we first improve the Merkle hash tree and design a novel tree named Merkle position index hash tree (MPIHT). For maintaining the same number of data blocks, MPIHT can reduce the height of the tree since it is able to store plenty of data blocks in every leaf. At the same time, the number of data blocks in every leaf would be changeably, thus supporting data deletion operation. Subsequently, we use MPIHT to propose a secure and provable deletion method for outsourced data. Our method can guarantee data integrity and achieve provable data deletion, requiring neither a trusted third party nor any complex calculations or protocols. Moreover, we demonstrate the security analysis to formally prove that our method can meet the expected requirements. Finally, we also implement our method to assess the performance. The assessment results disclose the efficiency advantages of our method over some existing methods.

The emergence of big data and artificial intelligence (AI) marks a significant milestone in technological advancement, impacting various sectors and transforming the essence of public and personal activities. Traditional machine learning, known as centralized learning, involves collecting data from multiple sources for the model development, which poses significant privacy risks. To address the conflict between the need for data sharing and the protection of privacy, federated learning (FL) has emerged as a promising solution. This approach allows for the continuous sharing of model updates between a central server and numerous local devices, fostering collaborative model development. However, it also brings about considerable communication costs and raises privacy concerns due to the potential for sensitive information leakage from the central server and local devices. To address these issues, we propose a lightweight and accuracy-lossless privacy-preserving FL scheme based on gradient clipping. The central server introduces noise to the global model to prevent clients from extracting valuable information, and then the local devices train these models using their data. To reduce the communication load, parameters with less impact on the model are removed using the Fisher information matrix. Additionally, to enhance privacy protection, the client-computed parameters are perturbed using the Diffie-Hellman key exchange method. Our experiments show that this approach greatly reduces the communication load for clients and the server, while effectively safeguarding client privacy and maintaining the model’s accuracy.

Vehicular Ad-hoc Networks (VANETs) face significant security and privacy challenges in modern intelligent transportation systems. This paper analyzes vulnerabilities in Al-Shareeda et al.'s vehicle authentication protocol (doi: 10.1109/TDSC.2025.3553868) and proposes an enhanced ECC-based scheme using short-lived pseudonymous certificates. We identify two critical weaknesses in Al-Shareeda et al.'s protocol—a desynchronization attack causing potential denial-of-service and an identity linking attack compromising vehicle privacy. Our protocol establishes mutual authentication between vehicles and roadside units, ensuring message integrity, anonymity, and perfect forward secrecy. Unlike existing approaches, it eliminates the need for online third-party authenticators. Formal security proofs demonstrate that the scheme's security is reducible to the hardness of the ECDLP and CDH problems. Performance analysis shows our approach achieves an optimal security-efficiency balance with competitive communication overhead (4608 bits) and computation costs (5.02 ms) compared to state-of-the-art alternatives, while uniquely satisfying all twelve evaluated security properties.

Forest fire detection and resource monitoring are the focus of forest resource protection, but they often suffer from internal attacks. Insider attackers try to get away with illegal activities such as illegal logging and encroachment on forest resources by manipulating data. Illegal activities pose a huge threat to forest resources, so defending against internal attacks is key to forest regulation systems. This paper presents a blockchain-based forest fire detection system (FFD) in a multi-party environment. FFD introduced the blockchain + database architecture for data storage and designed a hybrid encryption and decryption algorithm using CP-ABE and AES algorithms to realize data sharing and access control so as to ensure data security. The artificial intelligence technology is employed to identify fires, reducing the human factor in fires. Theoretical analysis and experimental data show that FFD has high performance and is suitable for forest fire detection and monitoring.

The development of the Internet of Medical Things has made online diagnostic systems an attractive application. However, the opacity and insufficient supervision of cloud computing can result in problems such as data leakage and unreliable outcomes in diagnostic systems. In order to solve these problems, this paper proposes a verifiable privacy-preserving online diagnosis scheme (VPOD) based on multiclass SVM. This solution uses CKKS leveled homomorphic encryption to build a secure inner product computation protocol and a secure decision function computation protocol to achieve privacy protection for multiclass SVM model parameters, patient medical data and diagnosis results. In addition, this article designs a verification mechanism for multiclass SVM model predictions to ensure the correctness of the results. Performance evaluation shows that VPOD is more functional, has low computational overhead for patients and hospitals, and has an accuracy of 98.64% on real data sets.

Addressing the challenge of untraceable malicious activities due to excessive anonymity in ring signature, we propose a new SM2-based ring signature scheme with revocability and anonymity (SMRSRA). Our scheme innovates by integrating a third-party-generated signer identity tag, a pivotal element in generating both signature values and a revocation tag within the SMRSRA. A key feature of our scheme is its revocation mechanism, which permits the third party to utilize the revocation tag, activating the anonymity revocation algorithm to reveal the signer’s identity. Furthermore, our scheme allows members to verify the third party’s actions for any malicious intent using the revocable anonymity tag. Experimental findings demonstrate that the time efficiency for signature generation and revocation in this scheme scales linearly with the number of ring members, ensuring its efficiency in scenarios involving numerous participants.

Federated Learning, as a distributed learning model, enables multiple clients to collaboratively train models while preserving data privacy. However, recent studies have highlighted a potential drawback: sharing gradient information could unintentionally lead to the exposure of private training data, allowing attackers to reconstruct this data from the shared gradients. To defend against this threat while maintaining high model accuracy, we propose a defensive method called Gradient Perturbation-based Dynamic Clipping (GPDC). This method mitigates the risk of gradient information leakage by introducing minor perturbations to the shared gradients and employing dynamic clipping techniques to preserve model accuracy. It combines two approaches: gradient perturbation and segmented clipping. Clients use an adaptive mechanism to dynamically trim gradients. After trimming, noise scales are adaptively added, with the noise determined by the clipping threshold and gradient changes. The effectiveness of the proposed defensive strategy was evaluated through experiments on the MNIST and CIFAR10 datasets. The results reveal that the GPDC method successfully resists DLG attacks while maintaining high model performance.

Edge computing serves as a potent solution for distributed learning tasks, offering benefits such as low latency, reduced network load, enhanced data security and privacy, adaptability to IoT development, and flexible deployment options. Introducing greater randomness and stability through the random selection of clients for communication in each iteration round enhances overall system performance. To solve the problem of high communication cost in traditional federated learning and model performance degradation in heterogeneous environments, we propose a lightweight decentralized federated learning framework, which uses soft threshold ternary quantization to compress local models. Soft threshold ternary quantization is a method to compress weight parameters into discrete values, which significantly reduces the complexity of parameter representation, thus saving storage space and communication costs. We devise an algorithm for selecting clients with similar data distributions to enhance model accuracy and expedite convergence in heterogeneous environments. Our framework ensures model performance while minimizing communication costs and safeguarding user privacy. Experiments show that compared with the existing ternary quantization federated learning method, the accuracy of the model we trained is improved.

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

Data aggregation technology plays a very important role in improving the efficiency of data collection of the Internet of Things (IoT). Most data collected by sensor nodes (SNs) in IoT is multi-dimensional. However, there are a few existing multi-dimensional data aggregation schemes, which are almost based on homomorphic encryption and not suitable for resource-constrained IoT smart devices. In addition, when SNs cannot upload in time for some reason, such as device error or network interruption, control center cannot get the correct aggregation result, i.e., robustness is not considered in most schemes. Therefore, we propose a lightweight and robust multi-dimensional data aggregation scheme for IoT. First, multi-dimensional data is packaged into one-dimensional data based on the Chinese Remainder Theorem, which greatly reduces communication and storage overhead. Second, the encryption in our scheme only uses addition operations without the costly additive homomorphic encryption. At the same time, our proposed scheme supports batch verification, which reduces the computation complexity of bilinear pairs by nearly half. Third, our scheme also supports dynamic SNs management and fault tolerance, enabling scalability and robustness. Finally, performance evaluation shows that our scheme has lower communication overhead and is more suitable for resource-constrained IoT scenarios.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by sending their local gradients to a server for aggregation, without having to disclose their data. The main security issues in FL, that is, the privacy of the gradient and the trained model, and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. A popular approach to protect the privacy of the gradient and the trained model is for each client to mask their own gradients using additively homomorphic encryption (HE). However, this leads to significant computation and communication overheads. On the other hand, to verify the aggregated gradient, several verifiable FL protocols that require the server to provide a verifiable aggregated gradient were proposed. However, these verifiable FL protocols perform poorly in computation and communication. In this paper, we propose SVFL, an efficient protocol for cross-silo FL, that supports both secure gradient aggregation and verification. We first replace the heavy HE operations with a simple masking technique. Then, we design an efficient verification mechanism that achieves the correctness verification of the aggregated gradient. We evaluate the performance of SVFL and show, by complexity analysis and experimental evaluations, that its computation and communication overheads remain low even on large datasets, with a negligible accuracy loss (less than$1\%$). Furthermore, we conduct experimental comparisons between SVFL and other existing FL protocols to show that SVFL achieves significant efficiency improvements in both computation and communication.

Cloud storage has benefited millions of users with its remarkable advantages of economy and flexibility. Since a single cloud service provider is not always reliable and prone to a single point of failure, multi-cloud storage is proposed to enhance data availability. However, cross multiple clouds (cross-cloud) auditing to provide users with the integrity verification of outsourced data also faces many challenges, such as the fact that a large quantity of existing schemes rely heavily on the trusted third-party. Therefore, we propose a decentralized data storage and integrity auditing scheme for multi-cloud scenarios to eliminate the dependence on the third-party, namely BDDR, and an improved version iBDDR with stronger security. First, both BDDR and iBDDR adopt multi-cloud data storage and support batch auditing to greatly save computation costs. Second, our schemes not only get rid of a third-party, but also do not require a dispute arbitration since the outsourced data can be verified by cloud server providers via the homomorphic verifiable tags published on the blockchain. Third, locating the corrupted cloud server providers and accurately recovering the corrupted data at a lower communication and computation costs are supported. Finally, security and performance analyses demonstrate the security and effectiveness of our schemes.

With the development of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in the aviation industry, a large amount of data are generated in ADS-B systems everyday. Cloud storage can satisfy the requirement of storing a large amount of ADS-B data well, however, the data stored on cloud server also raises problems about the security and confidentiality. This paper proposes a secure ADS-B data outsourcing scheme with integrity guarantee and multi-keyword retrieval (MRDP) to address these issues. In our MRDP, an index is generated for each piece of ADS-B data through Bloom filters to enable multi-keyword retrieval from clouds. Also, a unique label is produced for each piece of ADS-B data for integrity verification of query results. Our MRDP solution enjoys completeness property in that all query results with regard to the multiple keywords would be returned by the cloud server, otherwise it could be detected by the user. Security analysis indicated that our scheme offers integrity, completeness and privacy protection on outsourced ADS-B data, under the computational Diffie-Hellman (CDH) assumption and the discrete logarithm (DL) assumption. Theoretical and experimental analyses demonstrate the practicality of our proposed MRDP construction compared to existing solutions.

The current Single-User Key Derivation (SKD) caters to individual management of blockchain’s tree-structured assets but falls short for threshold signatures aimed at multi-party control of blockchain assets. We introduce a Distributed Key Derivation (DKD) for collaborative management of these assets. Our DKD aligns with SKD and the prevalent Decentralized Key Generation (DKG) in threshold signatures, employing the GG18 DKG [1] protocol to safely refresh child keys without affecting the parent key or other nodes. It maintains the hierarchical structure while preventing privilege escalation attacks. Experimental tests show that our DKD’s key derivation time is about 240 μs, minor compared to GG18’s DKG time.

Smart grid is the future direction of the traditional power system, with characteristics such as high efficiency and stability. Intelligent pricing, as part of the functionality of smart grids, relies on collecting a large amount of user power data to analyze and adjust electricity prices, which may lead to the possibility of user privacy data leakage. To address these issues, we propose a privacy-preserving scheme for smart grids that supports intelligent pricing (PSR). A data aggregation mechanism is designed based on Paillier encryption and Schnorr signature technology, which can protect the privacy of user data and realize efficient aggregation of user power data in the control center. In addition, a proxy re-encryption technology is developed to enable secure sharing of user power data between users and service providers. Based on security analysis and experimental evaluations, the PSR scheme has stronger security and higher efficiency compared to existing schemes.

With the rapid development of Internet of Things, the related data are growing explosively. However, IoT devices have limited storage and computing capabilities so that they cannot deal with massive data storage and computing locally. The integration of IoT and cloud is regarded as an effective solution to the above issue, i.e., IoT devices outsource collected data to cloud to enjoy powerful storage and computing resources. Because the data stored in cloud are out of the control of IoT users, some security risks need to be addressed in advance. In this paper, we propose a public data integrity verification scheme, called AIVCI, to check the data integrity for Cloud-IoT scenarios. Firstly, based on algebraic signature and homomorphic hash function, AIVCI can efficiently complete data auditing. Secondly, AIVCI adopts blind technology to prevent the privacy leakage of IoT data and further protect the privacy of IoT users. Thirdly, batch auditing is implemented to improve auditing efficiency and meet realistic demands for Cloud-IoT scenarios. And a new data structure named Improved Divide and Conquer Table (ID&CT) is designed to realize efficient data dynamics. Finally, the security and performance analysis demonstrates that AIVCI is more secure and efficient.

Location-based services (LBSs) are widely used in vehicular networks. Privacy leakage from LBS is a key issue to be solved. However, the existing schemes fail to provide differentiated protection for users’ different locations, which may lead to the leakage of location information. In this paper, we propose a personalized location privacy protection scheme based on differential privacy to protect the privacy of location-based services in vehicular networks. Firstly, we propose a normalized decision matrix to describe the efficiency and the privacy effect of navigation recommendations. We then establish a utility model integrated with users’ privacy preferences to compute the effective driving route. Secondly, for different service request locations in the driving route, we define sensitivity distance as an index to quantify their privacy requirements. The privacy budget will be added to the service request location to generate a false location. Moreover, due to the limitation of road range in the driving route, if the privacy budget value allocated is small enough, the false location generated by the Plane Laplace will be deviated. As a result, the attacker can deduce users’ real request locations. Consequently, considering the factors of trajectory leakage, attack strategy and QoS, we establish a multi-objective optimization model to optimize the false location. Based on the real data set, we conduct a series of comparison simulations to evaluate the performance of the proposed scheme. The experimental results demonstrate that our scheme can satisfy users’ personalized services needs and provide an optimal solution to privacy and QoS.

Edge networks are providing services for an increasing number of companies, and they can be used for communication between edge devices and edge gateways. However, the performance of edge devices varies greatly, and it is not easy to upgrade low-performance edge devices. Therefore, cyber attackers can use the vulnerability of edge devices to implement advanced persistent threat attacks. This article proposes a network verification framework for edge networks that can minimize the upgrades needed to strengthen edge network security. First, the communication parties use the data transmitted by the given edge network. Our method uses our proposed PacketVerifier to attach verification information to the packet after it is sent and to verify and restore the packet before it reaches the receiver. Second, due to the performance requirements of edge networks, we design a new data processing structure, namely, a sliding window double ring, to improve the performance of strict sequential protocols in parallel validation. Finally, experimental simulations show that our parallel processing algorithm has good performance in terms of network bandwidth compared with two existing packet processing algorithms. Furthermore, the proposed packet with verification information is compatible with the existing network topology, which helps PacketVerifier establish trustworthy transmission in a zero-trust environment.

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

With the rapid development of computer technology and medical imaging technology, medical images present an explosive growth. To save storage and computation overhead, hospitals often choose to outsource digital medical images to cloud server. Since medical images are a major auxiliary means for doctors’ diagnosis or medical researchers’ study, the secure retrieval of outsourced medical images is especially important. To address this problem, we propose a Faster outsourced Medical Image Retrieval scheme with privacy preservation (FMIR) in this paper. FMIR first makes a simple classification to outsourced medical images, which narrows the retrieval range and improves the retrieval efficiency compared with the existing unclassified retrieval schemes. Second, FMIR implements a lightweight access control for each class using polynomial-based access control strategy , which provides the fine-grained access control for better privacy protection of medical images. Third, FMIR reduces the interference of random numbers on relevant score to 0, which further improves the accuracy of the retrieval. Finally, the security and performance analysis show that FMIR is secure, accurate and efficient.

Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most.

With the popularity of cloud storage, increasing users begin to outsource data to the cloud. In order to resist possible data analysis for centralized outsourced data and improve the fault tolerance, users prefer to distribute data to cloud servers of different cloud service providers. However, once the data have been outsourced, it will be out of user’s control and many security issues may occur, such as outsourced data being illegally tamper with, or rarely accessed data being secretly deleted. In this article, we propose a decentralized self-auditing scheme for multi-cloud storage, called DSAS. First, based on the symmetric balanced incomplete block design, DSAS achieves integrity verification for outsourced data via the interactions of cloud servers and the auditing costs are shared by the participating CSs. Second, DSAS can locate misbehavior cloud server with low computation costs, and resist denial of service attack initiated by malicious cloud servers which attempts to destroy the audit. Third, DSAS can recover the corrupted data without fetching data, and support the revocation of cloud servers and batch auditing. Finally, security proof and function evaluation show that DSAS has comprehensive security and functionality, and performance simulations and experiment results show that DSAS is efficient.

Wireless body area network (WBAN) consists of a number of sensors that are worn on patients to collect dynamic e-health records (EHRs) and mobile devices that aggregate EHRs. These EHRs are encrypted at mobile devices and then uploaded to the public cloud for storage and user access. To share encrypted EHRs with users effectively, help users retrieve EHRs accurately, and ensure EHRs confidentiality, a secure multi‐keyword search and access control (SMKS-AC) scheme is proposed, which implements encrypted EHRs access control under the ciphertext-policy attribute-based encryption (CP-ABE). SMKS-AC provides multi‐keyword search for accurate EHRs retrieval, supports the validation of decrypted EHRs, and traces and revokes the identity of users who leak private key. Security analysis shows that SMKS-AC is secure against chosen keyword and chosen plaintext attacks. Through theoretical analysis and experimental verification, the proposed SMKS-AC scheme requires less storage resources and computational costs on mobile devices than existing schemes.

In unmanned aerial vehicle networks (UAVNs), unmanned aerial vehicles with restricted computing and communication capabilities can perform tasks in collaborative manner. However, communications in UAVN confront many security issues, for example, malicious entities may launch impersonate attacks. In UAVN, the command center (CMC) needs to perform mutual authentication with unmanned aerial vehicles in clusters. The aggregator (AGT) can verify the authenticity of authentication request from CMC; then, the attested authentication request is broadcasted to the reconnaissance unmanned aerial vehicle (UAV) in the same cluster. The authentication responses from UAVs can be verified and aggregated by AGT before being sent to CMC for validation. Also, existing solutions cannot resist malicious key generation center (KGC). To address these issues, this paper proposes a pairing-free authentication scheme (CLAS) for UAVNs based on the certificateless signature technology, which supports batch verification at both AGT and CMC sides so that the verification efficiency can be improved greatly. Security analysis shows that our CLAS scheme can guarantee the unforgeability for (attested) authentication request and (aggregate) responses in all phases. Performance analysis indicates that our CLAS scheme enjoys practical efficiency.

Monitoring the performance of a large network is very costly. Instead, a subset of paths or time intervals of the network can be measured while inferring the remaining network data by leveraging their spatiotemporal correlations. The quality of missing data recovery highly relies on the inference algorithms. Tensor completion has attracted some recent attentions with its capability of exploiting the multi-dimensional data structure for more accurate missing data inference. However, current tensor completion algorithms only model the three-order interaction of data features through the inner product, which is insufficient to capture the high-order, nonlinear correlations across different feature dimensions. In this paper, we propose a novel Neural Tensor Completion (NTC) scheme to effectively model three-order interaction among data features with the outer product and build a 3D interaction map. Based on which, we apply 3D convolution to learn features of high-order interaction from the local range to the global range. We demonstrate this will lead to good learning ability. We conduct extensive experiments on two real-world network monitoring datasets, Abilene and WS-DREAM, to demonstrate that NTC can significantly reduce the error in missing data recovery. When the sampling ratio is low at 1%, the recovery error ratios on the testing data are around 0.05 (Abilene) and 0.13 (WS-DREAM) when using NTC, but are 0.99 (Abilene) and 0.99 (WS-DREAM) using the best current tensor completion algorithms, which are 21 times and 8 times larger.

Many Internet service providers are still using traditional password strength evaluation methods, resulting in user passwords being vulnerable to social engineering attacks. We believe that the password strength evaluation method based on sensitive personal information has great research value for improving the security of password authentication system. In this paper, we use the structure segmentation algorithm and the bidirectional matching algorithm to investigate how users' personal information is used in passwords. Then, we present a sensitivity personal information coverage evaluation function that represents the correlation between users' password and their personal information. Finally, a password strength evaluation method based on sensitive personal information is proposed. This method is composed of three stages: preprocessing stage, prediction dictionary generation stage and password strength evaluation stage.

Smart grid can greatly improve the efficiency, reliability, and sustainability of the traditional grids. In industrial smart grid, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. However, the procedure of frequently metering data collection may lead to sensitive information leakage. To address the security issues in industrial smart grid, in this article, we construct an efficient identity-based metering data aggregation scheme supporting batch verification by collector and electricity service provider, respectively, which guarantees the privacy and integrity of metering data. In our scheme, collectors are allowed to collect and aggregate the metering data of users in their respective administrative domain without compromising the confidentiality of metering data. Security analysis demonstrates that our proposed scheme is provably secure in the random oracle and satisfies the above security requirements. Performance analysis indicates that our scheme outperforms existing solutions in terms of communication and computation costs.

The connected vehicular ad hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free, since their combination brings many new security and privacy requirements for VANET applications. In this paper, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments.

In this paper, we focus on the security of outsourced database.A verification scheme for outsourced database is proposed based on the verifiable polynomial technique.In this scheme, we consider the encrypted database.The outsourced high degree polynomial will enhance the authenticity of the data.If the cloud server returns fake data, it will be detected by the clients easily.

Summary Recently, some existing works have introduced novel way to construct complex networks from embedded time series, which provides new sights into the organizational properties of the time series in phase space. In this paper, we attempt to answer the fundamental question of “how much information regarding the dynamic property of the original time series can be extracted from these networks.” To this end, we propose a new method for reconstructing time series from the networks. We compare the reconstructed time series from these networks and that from the recurrence plot. We find that these networks contain topological information of the embedded time series to a certain degree. In general, they are more powerful than the recurrence plot method in the reconstruction of embedded time series. In addition, we study a new generalized projective synchronization (GPS) of coupled complex dynamical networks with different sizes via feedback control and impulsive control. Based on the stability analysis of impulsive system, a network synchronization criterion is established. These works may find potential application for secure communication via networks.

In the era of information explosion, people have to deal with huge amount of data.It is a great computation burden for the resourceconstrained clients.Cloud computing connects large amounts of network resources, and forms a vast pool of resources.It provides much convenience for people.Clients can outsource the complex computation task to the powerful cloud server.In this way, the computation burden of clients can be greatly reduced.In this paper a new algorithm of secure outsourcing for polynomials is proposed.In the computation process, the computation polynomial is hidden to cloud server, and the inputs and outputs of polynomials will not revealed.In addition, clients can verify the result easily.

Aggregate signature algorithms combine n signatures on n different messages from n distinct users into one aggregated signature. The aggregated signature allows the verifier to authenticate the n signatures simultaneously. Because the total signature length and authentication costs are significantly reduced, aggregate signature algorithms are attractive to applications with resource constraints and applications requiring efficient batch authentications. In this paper, we propose a novel aggregate signature scheme based on certificateless-PKC. Under this novel scheme, the length of the aggregated signature and the pairing computation cost in the aggregate signature verification process are independent of the number of signatures being aggregated. We also prove that the proposed scheme is existentially unforgeable against adaptive chosen-message and chosen-identity attacks, based on the hardness assumption of the computational Diffie-Hellman problem. The new scheme will be suitable for resource-constrained applications. Copyright © 2016 John Wiley & Sons, Ltd.