Internet of Things (IoTs) based smart grid systems require the implementation of diverse functionalities, data privacy protection, and lightweight solutions for terminal devices. Additionally, data aggregation plays a critical role within IoT-based smart grid systems. Currently, homomorphic encryption, a common data aggregation method, is widely adopted to safeguard personal data privacy. However, there are significant challenges associated with utilizing homomorphic encryption for smart grids. Firstly, homomorphic encryption can hinder the efficient processing capabilities of electric utilities, impacting the critical time required to meet specific functional requirements. Secondly, it imposes substantial computational overhead on edge devices, making it unsuitable for grid applications requiring practical, lightweight solutions. To address these barriers, we present a data aggregation scheme that is based on the separation of feature information. We initially employ the Horner’s rule aggregation method to aggregate users’ personal data before transmitting it to the control center, which can efficiently complete functions such as load forecasting and precise pricing by reverse engineering Horner parameters to obtain granular user data. In addition, we separate users’ individual electricity consumption data from their identity labels, using the Fisher-Yates Shuffle algorithm to shuffle the association between electricity consumption data and the corresponding user identity labels. This method ensures that the control center can only access granular electricity consumption data, without knowledge of the associated user identities. The security analysis demonstrates that the proposal effectively resists forgery, rollback, collusion, and eavesdropping attacks. Both theoretical and experimental analyses indicate that the computational overhead of this scheme on edge devices meets the practical requirements of power grid systems. Importantly, the experimental simulations indicate that our scheme improves the execution time of extended functions by a factor of 102and greater.

With the rapid advancement of Internet of Things (IoT) and the growing complexity of network infrastructures, the traditional network management model based on manual configuration is no longer able to meet the dynamic IoT secure communication requirements. Intent-based networking (IBN) significantly improves network manageability and agility by driving network automation through high-level business intent. However, policies in IBN are highly dynamic, and issues such as frequent changes in user intent, role or attribute adjustments, and ad-hoc access requirements in multi-tenant environments make it necessary for the system to quickly adapt to policy changes to ensure secure data sharing. Attribute-based encryption has become a key enabler for securing IoT communications, offering fine grained access control and adaptability to users’ dynamic access requirements in real time. In this paper, we propose an attribute-based access control supporting policy reconfiguration (ABACPR) for the highly dynamic nature of policies in IBN, aiming to improve the flexibility and efficiency of policy updating and ensure the security and consistency. Finally, We compared the theoretical and experimental analysis with related works and the results demonstrate that ABACPR is better suited for secure IoT communication in IBN.

Bitcoin mixing operations are essential for enhancing transaction privacy by obfuscating the link between transaction inputs and outputs. However, the transparency of blockchain transactions presents significant privacy risks, as transaction details are publicly recorded. To address these challenges, Bitcoin mixing operations are crucial for obfuscating the link between transaction inputs and outputs, thereby enhancing transaction privacy. Whereas, current mixing approaches are limited by several issues, including reliance on trusted third-party services, vulnerability to forgery by malicious participants, and suboptimal efficiency. In response to these challenges, we propose a decentralized Bitcoin mixing scheme named CoinMixMultiSig (CMMS). This scheme designs a two-round collaborative public address generation process, allowing participants to jointly create a public address and send their Bitcoin efficiently to this address before signing. This approach mitigates fraud risks, such as participants dropping out during the mixing process or maliciously altering transaction data, which could lead to asset loss. To enhance security and efficiency, CMMS incorporates a multi-signature mechanism that reduces the overall signature size. The multi-signature mechanism reduces computational and communication overhead, enhancing efficiency and practicality for real-world use. A rigorous security analysis demonstrates that CMMS ensures unforgeability and effectively resists rogue key attacks, providing a reliable and secure option for Bitcoin transactions.

To ensure data robustness, users typically create replicas of critical data and store them on multiple servers. However, due to server failures or malicious attacks, these replicas may face the risk of loss or tampering. Therefore, periodic auditing of data replicas is necessary to ensure their integrity. Existing solutions perform periodic auditing with generating metadata for each replica individually. However, generating auditing metadata for every replica incurs significant computational overhead, and storing metadata for multiple replicas also imposes a considerable storage burden. To reduce this overhead and optimize the efficiency of auditing towards multiple replicas, we propose collusion-resistant multi-replica data auditing with optimized metadata generation (CRMRDA). First, CRMRDA enhances replica generation efficiency by employing symmetric encryption and random masking. An indistinguishable challenge strategy is introduced, making the number of generated auditing metadata copies independent of the number of replicas. Second, we construct a security model for multi-replica data possession that allows server collusion and provide a formal security proof for CRMRDA. In addition, CRMRDA supports practical features such as dynamic operations and batch auditing. Finally, the efficiency of CRMRDA is evaluated in an experimental environment consisting of four servers, one third-party auditor, and one data owner. Compared to the latest multi-replica data auditing schemes, CRMRDA demonstrates lower computational overhead in replica generation, metadata generation, and periodic auditing.

Public key authenticated encryption with keyword search (PAEKS) is a cryptographic primitive applicable in cloud storage systems. It empowers cloud servers to conduct searches on encrypted data without decryption while safeguarding against the brute-force attack known as insider-keyword-guessing attacks (IKGAs). In contrast to the pioneering primitive PEKS, which is vulnerable to IKGAs, PAEKS incurs additional computational and communication overhead due to the sender keys' involvement in encryption and trapdoor-generation processes. Although the recent work improves the efficiency of PAEKS by re-encrypting received ciphertexts, the requirement of a fully trusted proxy is rather costly for users to implement in practice. To reduce the economic cost and to keep a high efficiency, we propose a new primitive ofProxy-free Public-key Authenticated Updatable and Searchable Encryption(PF-AUKS). The key concept is to let the cloud server, instead of the proxy, directly convert different-source ciphertexts into a uniform format securely. We propose a concrete PF-AUKS scheme that supports fast search, constant trapdoor generation, and secure ciphertext update. Theoretical evaluation and experimental results illustrate high algorithm running speed and retrieval efficiency. We formally define the security model of PF-AUKS and prove that our scheme is secure under this model.

With the rapid advancement of cloud technology, ciphertext-policy attribute-based encryption (CP-ABE) schemes are highly suited to cloud storage environments. In order to protect sensitive information, policy-hidden CP-ABE has garnered significant attention. However, these schemes are vulnerable to fake policy attacks, where an attacker may introduce false policy and leak system information. To address this issue, we propose a traceable and revocable CP-ABE scheme with policy authentication (TR-PA-ABE). This scheme incorporates a policy checker, which is able to verify whether a ciphertext is encrypted under the correct access policy without revealing any confidential information. Additionally, it features a traceability mechanism that leverages white-box tracing to identify users who leak their keys by embedding user identities within their attribute keys. Our direct revocation method efficiently updates ciphertexts associated with revoked users without impacting the keys of other users, thus minimizing computing overhead. We formally prove that TR-PA-ABE is indistinguishable secure under chosen plaintext attacks (IND-CPA) based on the decision parallel$q$-bilinear Diffie-Hellman exponent assumption. Furthermore, our performance evaluation illustrates the practicality and efficiency of TR-PA-ABE.

Hybrid satellite networks, composed of Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) systems, are capable of ensuring seamless and flexible data exchange across entities. However, the inherent heterogeneity presents critical challenges for cross-domain data aggregation. Specifically, the following issues remain unsolved for current cross-domain data aggregation designs, including insufficient adaptability to the dynamic hierarchical network topologies, inflexible leader election for intra-domain data aggregation, and unsound privacy preservation for inter-domain data transmission. To overcome these limitations, a cross-domain heterogeneous data aggregation scheme for hybrid satellite networks is developed, providing dynamic group key agreement. First, an efficient re-authentication mechanism is constructed to ensure de-synchronization resistance. Meanwhile, a flexible and adaptive leader election strategy is proposed to enhance stable and seamless data exchange among dynamic LEO networks. Additionally, a secure dynamic cross-domain data transmission method is designed to resist eavesdropping and replay attacks. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

In recent years, numerous group authentication and key agreement (GAKA) schemes have been proposed for the Industrial Internet of Things (IIoTs). However, the frequent identity updates that are a feature of IIoT environments mean that existing schemes cannot maintain full-lifecycle device anonymity. Meanwhile, static integrity verification approaches cannot effectively cope with the dynamic and heterogeneous nature of industrial networks, making it imperative to design an adaptive data integrity protection mechanism to maintain reliable and resilient communication. Additionally, the simultaneous access of a large number of intelligent industrial devices to gateway nodes creates significant computational and communication challenges, while also increasing the risk of denial-of-service attacks and other concurrent threats. Consequently, current schemes are unable to strike an optimal balance between efficiency and security in large-scale IIoT deployments. In our scheme, we first designed an anonymous token mechanism to achieve sufficient randomness for unlinkability when communicating with the same gateway node across different time periods. Secondly, integrating associated data into the authenticated sponge construction (ASCON) encryption process ensures the legitimacy and integrity of the data during transmission. Third, we conduct rigorous security proofs under the widely accepted Algebraic Group Model (AGM) and Random Oracle Model (ROM), demonstrating that our group authentication mechanism is unforgeable against adaptive chosen-public-key and adaptive chosen-subspace attacks. Finally, the performance evaluation demonstrates that our proposed scheme achieves improvement in computational efficiency, while the communication overhead analysis confirms that the design remains both efficient.

Vehicular ad-hoc networks (VANETs), considered a pivotal component of intelligent transportation systems (ITS), are susceptible to both established and emerging security vulnerabilities. However, existing authenticated key management schemes fail to provide effective conditional anonymity during decentralized authentication process. Meanwhile, scalable and reliable vehicular pseudonym management is absent, resulting in potential privacy leakage. Furthermore, conventional group key agreement schemes inherently fail to properly accommodate the highly dynamic topological characteristics of vehicular environments, which significantly limits their practical applicability. To address these challenges, the blockchain-assisted anonymous authentication and tree-based group key agreement design is proposed in this paper. Firstly, the pairing-free decentralized authentication mechanism is designed to enable mutual authentication between vehicles and roadside units (RSUs). Secondly, the threshold-varying pseudonym management system is designed, leveraging secret sharing and smart contracts to ensure conditional privacy preservation. This mechanism utilizes the multi-RSU consensus to recover the user's real identity, enabling traceability of malicious entities. Thirdly, the self-balancing tree-based group key agreement mechanism is proposed, optimizing key generation efficiency in dynamic vehicular environments. Crucial security requirements can be satisfied via the security analysis, whereas the performance evaluation substantiates the superiority of the proposed scheme over existing approaches.

With the rapid growth of mobile applications, large volumes of private data are continuously generated on mobile devices and increasingly outsourced to cloud storage services. Due to the limited computational and energy resources of mobile devices, enabling efficient and secure periodic auditing of cloud stored data remains a challenging task. Existing cloud data auditing schemes, such as Provable Data Possession and Proof of Retrievability, require computation-intensive operations that are impractical for resource-constrained mobile environments. To address this challenge, we propose MIDAS, a mobile-friendly data auditing scheme that supports secure outsourcing of auditing metadata with balanced resource utilization. MIDAS adopts a two-phase auditing metadata structure, in which computation intensive operations are offloaded to edge nodes, while mobile devices only perform lightweight processing. In addition, identity information of edge nodes is embedded into the auditing metadata to support traceability and accountability. To further improve scalability, we design a resource scheduling model for Multi MU and Multi-EN scenarios and propose the proportional load balanced scheduling algorithm to achieve efficient and load balanced metadata generation. The security of MIDAS is rigorously proven by reducing it to the discrete logarithm problem in the RSA quadratic residue subgroup within the random oracle model. Experimental results in a real-world environment demonstrate that MIDAS significantly reduces computational overhead on mobile devices, maintains acceptable communication cost, and achieves superior scalability compared with existing schemes.

Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks. In this paper, we present TOKENSCOUT, the first temporal GNN-based framework for scam token early detection. TOKEN SCOUT formulates token transfer data as a dynamic temporal attributed multigraph and leverages the temporal graph learning model to learn graph representations. It also builds a graph rep resentation refining model based on contrastive learning to learn a more discriminative representation space for risk identification. We evaluated TOKENSCOUT using a comprehensive dataset of 214,084 standard ERC20 tokens from 2015 to February 2023. TOKENSCOUT achieves a balanced accuracy of 98.41%. Additionally, from March to May 2023, deploying TOKENSCOUT on Ethereum effectively identified 706 rugpulls, 174 honeypots, and 90 Ponzi schemes, thereby alerting to potential risks exceeding $240 million.

Deep learning based side-channel analysis (DL-SCA) has achieved remarkable success in recovering cryptographic keys from embedded devices by exploiting physical leakages such as power consumption and electromagnetic emissions, posing a serious threat to the security of cryptographic implementations. However, a major challenge arises in cross-device attacks, where a model trained on profiling devices cannot be directly applied to attack a different device. This is because domain discrepancies emerge from variations in chip architecture, manufacturing, operational conditions, and data acquisition methods between these two devices. Many existing DL-SCA schemes have not adequately addressed the challenges posed by the differences. Therefore, we propose a novel cross-device SCA framework based on an autoencoder, which leverages the encoder—decoder architecture to align feature distributions across devices in the latent space while simultaneously preserving discriminative leakage features through the reconstruction process. To achieve this, Maximum Mean Discrepancy (MMD) is integrated into the loss function and applied to the latent representations, effectively narrowing the distribution gap between profiling and attack devices. Operating in the latent space allows our approach to avoid the training instability of adversarial methods and provides an efficient end-to-end solution for domain alignment. Building on this framework, we further introduce three multi-domain adaptation methods. Experimental results, evaluated in terms of Partial Guessing Entropy (PGE), demonstrate that cross-device attacks can be effectively executed even with device discrepancies, with most keys being successfully recovered within 1,000 traces. Moreover, the proposed adaptation techniques significantly reduce the number of traces required for successful key recovery.

In recent years, deep learning side-channel analysis (DLSCA) has garnered significant attention, with the choice of model architecture greatly influencing attack efficiency. Currently, convolutional neural networks (CNNs) have become the dominant architecture in the field of side-channel analysis (SCA), and multi-scale CNNs (MCNNs) have gained favor among certain researchers due to their ability to capture information across various scales. However, effectively obtaining multi-scale information from datasets requires the customization of appropriate hyperparameters for each channel, and the hyperparameter tuning process is often time-consuming and labor-intensive. This presents a technical barrier for non-experts or those seeking to simplify their workflow. Such limitations lead researchers to overly rely on fixed hyperparameter models based on specific datasets, overlooking the differences between various data samples, which in turn affects the model’s reusability and generalization capability in broader scenarios. To address these issues, we propose an adaptive MCNN framework based on automated machine learning, named Auto-MCNN. We evaluated the effectiveness of this framework on multiple private and public datasets. To further investigate the variations in the network’s feature extraction capabilities, we employed an improved heatmap visualization method to illustrate the network’s areas of focus. Experimental results demonstrate that the optimized Auto-MCNN model can be more widely applied to the analysis of side-channel leakage traces, significantly enhancing overall analysis efficiency.

Anonymous authentication mechanisms play an increasingly critical role in digital ecosystems by enabling users to prove eligibility without revealing identity information. Anonymous tokens serve as fundamental cryptographic primitives for privacy-preserving access control. However, existing solutions often rely on trusted hardware or suffer from centralization issues, such as single points of failure (SPoF) and strong trust assumptions, to enforce non-transferability. In this work, we construct a threshold BBS+ signing protocol using verifiable multiplication-to-addition (MtA) techniques derived from vector oblivious linear evaluation (VOLE). The security of the proposed threshold signature scheme is rigorously established within the Universal Composability (UC) framework. Building upon this foundation, we introduce the first threshold-authorized and threshold-redeemable, and non-transferable anonymous tokens named T3AT. T3AT enables collaborative issuance and verification in the malicious adversary and dishonest majority settings while achieving non-transferability, unlinkability, and unforgeability without relying on trusted hardware or centralized authorities. Our performance evaluation demonstrates the practicality, efficiency, and scalability of T3AT, effectively bridging the gap between anonymous tokens and threshold-based authorization and authentication for privacy-enhanced access control.

As cloud computing advances, data owners increasingly upload large volumes of data to the cloud. Attribute-based searchable encryption (ABSE) empowers data owners to manage fine-grained access over encrypted cloud files, and supports keyword-based search for authorized users. However, current multi-owner searchable encryption schemes often suffer from efficiency limitations and vulnerabilities to keyword guessing attacks. Furthermore, access policies are typically stored in plain form, exposing confidential details about data owners and authorized users. To tackle the aforementioned issues, we put forward an expressive attribute-based searchable encryption scheme with full policy concealment. Our design leverages the reduced ordered binary decision diagram (ROBDD) for access control targeting multi-user and multi-owner environments. In our scheme, users can flexibly select data owners and utilize a single trapdoor to search across shared datasets. The integration of a warrant server that signs obfuscated keywords prevents the cloud server from launching effective keyword guessing attacks. The adoption of ROBDD enables complex access policies via boolean operations, thereby significantly enhancing the efficiency and flexibility of access control. Full policy hiding is achieved by mapping ROBDD paths to an improved bloom filter, preventing access policy leakage. We present formal definitions and security models of the proposed approach, along with rigorous security proofs. Performance evaluation is conducted through theoretical analysis and simulations. Experimental indicate that our scheme achieves superior efficiency over state-of-the-art alternatives, offering a robust solution for secure and flexible cloud data management.

As Flying Ad-hoc Network (FANET) evolves toward larger scales and higher levels of autonomy, the importance of secure and efficient group communication continues to grow. However, resource-constrained unmanned aerial vehicles (UAVs) face dual challenges: limited computational power struggles to meet the high demands of complex cryptographic algorithms, while bandwidth constraints exacerbate communication overhead caused by multi-round interaction mechanisms. Moreover, existing solutions find it hard to support dynamic group environments and are prone to single point of failure (SPoF) in centralized architectures, which significantly compromises system reliability and scalability. To address these issues, this paper proposes a novel key agreement protocol for FANET. The protocol employs an improved tree-based key encapsulation mechanism (iTreeKEM) to support rapid key updates in highly dynamic environments. It reduces the computational cost for each group member by 90.08% even when the group size reaches 128. To further enhance system robustness, the protocol introduces a smart contract-based distributed leader election mechanism, effectively eliminating SPoF. The security of the proposed protocol is guaranteed by the CDH problem under the generalized selective decryption (GSD) model. Finally, we implement the protocol in NS-3 simulations, and the results demonstrate its effective applicability to FANET.

The rapid expansion of mobile communication networks has led to a surge in cellular traffic, highlighting the need for advanced prediction models to improve network performance. Federated learning (FL) offers a promising solution by enabling distributed model training across multiple nodes, aligning well with the decentralized nature of modern networks. However, applying FL to spatiotemporal cellular traffic prediction is challenging due to the substantial communication overhead in distributed learning. To address this, we propose LFedSG, a lightweight FL framework incorporating subgraph partitioning for spatiotemporal traffic prediction. LFedSG supports collaborative training while preserving inter-client dependencies critical for accurate prediction. Communication efficiency is achieved by focusing on essential model parameters, while subgraph partitioning and spatiotemporal graph convolutional networks (STGCN) enhance spatial and temporal correlation modeling. An adaptive transmission weight pruning strategy further reduces communication and computation costs. Extensive experiments on the Telecom Italia and Pems07 datasets demonstrate that LFedSG achieves higher predictive accuracy than traditional methods, with significant reductions in communication overhead and training time, validating its effectiveness and scalability for large-scale mobile network environments.

Online ride-hailing services (ORHS) are changing the travel mode. The quality evaluation of ORHS is essential to regulate driver behavior and guide passengers in choosing good services. The existing quality evaluation methods of ORHS rely on subjective passenger feedback, while they are susceptible to malicious or paranoid feedback, resulting in untrustworthy evaluation results. This paper proposes a non-subjective trust mechanism for ORHS to supplement existing evaluation methods. Inspired by the trust machine, this mechanism defines the concept of non-subjective trust to measure the quality of ORHS. It uses trajectory data collected by infrastructure as a parameter to calculate the non-subjective trust value of ORHS, which can ensure the trustworthiness and authenticity of the calculation results. This mechanism also improves the CKKS homomorphic encryption algorithm to ensure both the privacy protection of the trajectory data and the effective calculation of non-subjective trust values. In addition, a blockchain is adopted to store trajectory data cipher text and trust values plaintext in the infrastructure. It promotes its flexible management and use and ensures the security of original data and traceability of evaluation results. Theoretical analysis and experiments show that the trust value calculated by this mechanism is trustworthy, and its time costs are feasible.

Portability poses a significant challenge for Deep Learning (DL)-based profiling Side-Channel Analysis (SCA) on AES encryption, as attackers cannot always ensure that training and target samples use the same encryption mode. To address this, we propose an Unsupervised Domain Adaptation (UDA) DL-SCA framework for achieving effective and robust cross-encryption-mode attacks. By incorporating cross-attention and UDA techniques, our framework aligns high-dimensional input samples, reducing interference from encryption mode mismatches. Evaluation across five distinct AES modes demonstrates that our method achieves robust SCA performance without requiring prior knowledge or multiple labeled datasets for analysis.

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

Many businesses are putting their sensitive data in the cloud with the fast growth of cloud computing and storage. To ensure user privacy, it is necessary to keep encrypted data only in the cloud. Attribute-based encryption (ABE) is a popular mean in cloud storage scenarios. ABE is not only faced with key escrow problem but also suffers from user revocation issue when he or she is no longer authorized to access to encrypted data. In order to address these two issues, we propose a revocable registered attribute-based encryption scheme, which not only avoids key escrow problem but also supports precise revocation of a user’s access to a file as well as permanent deregistration of a user from the system. Furthermore, we prove the semantic security of the scheme and conduct a performance experiment to show the efficiency.

Industrial Internet of Things (IIoT) processes industrial information anytime and anywhere by deploying smart devices, which inevitably confronts with potential challenges for access control and secure authentication issues. Attribute-based signature (ABS) utilizes a collection of attributes instead of the user’s identity to achieve identity authentication, which supports anonymous access control, data integrity and nonrepudiation. However, ABS schemes exist inherent key escrow problem because all users’ private keys are generated via key authority. In addition, most ABS schemes use time consuming pairing operations, which is unsuitable for resource-constrained IIoT devices. To solve above problems, we present a key escrow-free ABS scheme and utilize server-aided technology to run most of pairing operations in the verification phase, which reduces computation overhead in recursive algorithm based on tree. Furthermore, we utilize tree-based access policy to implement flexible access control. We design a key distribution protocol. By executing this protocol, the key authority cannot derive a whole private key independently without no user’s secret value, which solves key escrow problem. We demonstrate that the presented scheme is existentially unforgeable under adaptive chosen-policy attack in the standard model. Performance analysis shows that the designed scheme is more efficient compared with the existing ABS schemes.

As Internet technology develops swiftly, the significance of privacy protection is escalating in the realms of e-commerce, e-government and software security. Due to the combination of the benefits of proxy signatures and blind signatures, the proxy blind signature scheme not only distributes the workload across application networks but also effectively safeguards the confidentiality of sensitive information. Additionally, the identity-based proxy blind signature protocol can avoid the problem of heavy certificate management and is widely used in electronic commerce and other scenarios. However, some identity-based proxy blind signature protocols that rely on the large integer factorization problem and the discrete logarithm problem are unable to withstand from attacks from quantum computers. Furthermore, current lattice-based proxy blind signature protocols offer only heuristic security and require three rounds of information exchange during the signing phase. In this paper, we introduce a new two-round identity-based proxy blind signature scheme based on lattices. This scheme utilizes a zero-knowledge proof protocol on lattices as its core component to develop an interactive two-round signature scheme that is free from security proof vulnerabilities. Additionally, we validate the security of the proposed protocol within the random oracle model and conduct a performance analysis.

Ciphertext-policy attribute-based encryption (CP-ABE) has attracted significant attention due to its fine-grained access control capabilities, which are highly compatible with cloud computing. Most enterprises utilizing cloud storage technology consist of multiple user groups. However, the current multigroup CP-ABE scheme may pose a risk of sensitive information leakage due to the plaintext access policy mechanisms. To mitigate this issue, it is necessary to conceal access policies. In this article, we propose a flexible policy-hidden multigroup attribute-based encryption (PH-MG-ABE) scheme that enables unique multigroup operations, such as group merging and splitting without affecting user keys. Each attribute in the access policy is divided into attribute values and attribute names. The proposed scheme achieves partial policy hiding by concealing the attribute values. Our scheme allows to directly revoke and join arbitrary numbers of users. In order to reduce the local decryption burden for users, the heavy decryption tasks are outsourced to cloud servers and correctness of the outsourced decryption is verifiable. We prove that our scheme is indistinguishable against under chosen plaintext attacks secure (IND-CPA) based on the decisional q-bilinear Diffie-Hellman exponent assumption. In addition, the proposed scheme appears to be efficient through the performance evaluation.

Decentralized payment systems, such as Bitcoin, enable immutable, and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this article, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation.

Vertical federated learning (VFL) shows promise for enabling collaborative learning among Internet of Vehicle systems (IoVs) without requiring the sharing of private training data. However, existing work has exposed VFL’s vulnerability to privacy-stealing attacks, where an honest but curious server might reconstruct a client’s raw data from client-uploaded embeddings. In this work, we first elucidate the intrinsic mechanisms of privacy attacks from an information theory perspective, which provides a solid foundation for potential defensive strategies. Based on our findings, we introduce PriVFL, a defense mechanism based on information bottleneck theory. PriVFL is designed to safeguard the privacy of VFL-based IoVs by enabling shared embeddings to extract minimal information from input data, while preserving the information essential to target labels. Specifically, PriVFL restricts the information contained in embeddings by reducing the upper bound of mutual information between the raw samples and embeddings uploaded from local clients. Meanwhile, PriVFL ensures the effectiveness of the model by increasing the mutual information lower bound between embeddings and samples’ labels. Our evaluation includes 5 benchmark data sets and 4 different models. Experimental results demonstrate that PriVFL effectively mitigates privacy attacks while preserving the model’s effectiveness. These findings underscore that PriVFL can significantly enhance the privacy of VFL-based IoVs, thereby bolstering the development of practical IoV applications.

In cryptography, side-channel analysis (SCA) is a technique used to recover cryptographic keys by examining the physical leakages that occur during the operation of cryptographic devices. Recent advancements in deep learning (DL) have greatly enhanced the extraction of crucial information from intricate leakage patterns. A considerable amount of research is dedicated to studying the SubByte (SB) operations of the advanced encryption standard (AES). This is because the SB process, which generates numerous transitions between 0s and 1s during encryption, results in significant energy leakage. However, traditional analysis models primarily focus on the initial round of SB operations in AES, which are less effective on mobile terminals where it is difficult to collect enough signals. These models often neglect additional operations and subsequent rounds, thus providing limited insights from small datasets. Consequently, this limitation has a direct impact on the accuracy and efficiency of key recovery. Our study uses$\rho $-test analysis to show that significant leakage occurs not only during the S-box operation but also during the AddRoundKey (AR) phase of AES. To address these challenges, we propose a new SCA method, that is, optimized for small sample sizes. This method includes a new comprehensive round trace labeling algorithm, which simultaneously analyzes the SB and AR stages of each AES round. Additionally, we introduce the peak precise localization algorithm to accurately identify the points of energy leakage during each encryption round. Our experiments, conducted with power and electromagnetic (EM) datasets from the STM32F303 microcontroller, demonstrate that our method can reliably recover keys with as few as 20 traces. These results highlight the enhanced capability of our method in handling the complexities of small sample datasets in cryptographic analysis.

Secure aggregation of medical data enables detailed data analysis and informed medical decision-making in e-health systems, optimizing data resources utilization and enhancing service quality and decision accuracy. However, the collection of large volumes of medical data poses a significant risk of privacy leakage. Most existing privacy-preserving data aggregation schemes focus on additive aggregation of single or multi-dimensional data, which greatly limits their applicability. This article introduces a blockchain-based multifunctional data aggregation (BM-PDA) scheme for e-health systems. First, BM-PDA supports overall aggregation queries of data samples and can compute the maximum and minimum values within these samples. Second, it enables selective data aggregation queries based on various user attributes. Furthermore, analysis shows that integrating these two algorithms protects both user’s private data and attribute data. Performance evaluations indicate that the computational and communication costs are acceptable, demonstrating the scheme’s practical applicability.

In cloud Electronic Medical Records (EMRs), health-related private information such as genetics and diseases is contained. Thus, the secure ownership transfer protocol should protect users’ privacy. In certain scenarios, some users, including patients, doctors, medical and research institutions, may be offline. As a result, existing protocols cannot be directly employed. Motivated by these observations, in this paper we propose a secure and efficient ownership transfer for cloud EMRs auditing protocol. Specifically, our protocol allows the existence of offline users while ensuring users anonymity, it is achieved using different signature constructions. Additionally, a tracing mechanism is introduced to safeguard against malicious users. We rigorously prove the security of our protocol, comprehensively evaluate the performance of it, and compare our protocol with a few closely relevant protocols. According to the evaluations, our protocol significantly improves ownership transfer efficiency while achieving additional functionalities, including public verifiability, multi-ownership transferability, anonymity, and traceability.

Ciphertext-policy attribute-based encryption (CP-ABE) has long been considered as a promising access control technology for cloud storage. However, CP-ABE depends on a central trusted authority to generate and distribute decryption keys, resulting in the key escrow issue. Most existing solutions only mitigate this problem but fail to resolve it entirely. Registered attribute-based encryption (RABE), a new cryptographic primitive, fundamentally addresses the key escrow problem by modifying the trust model, but its high computational overhead limits its practical application. Inspired by this challenge, we present an efficient registered attribute-based access control scheme designed for data encrypted with access policies containing the same sub-policy. In our scheme, users generate their own keys, while a key manager, who does not hold keys, replaces the central authority in managing users. Additionally, for data encrypted with the same sub-policy, the user’s initial decryption stores the relevant parameters, which can be used for subsequent decryptions to reduce computational overhead. The proposed scheme is proven to achieve semantic security. Performance analysis demonstrates that our scheme enhances decryption efficiency by roughly 41.4$\%$compared to existing RABE scheme, with a minimal storage trade-off, making it more practical for cloud storage application.

Efficient power management in smart grids relies on regularly obtaining the electricity usage of each user. However, the aggregation of the electricity usage data may expose user privacy. At present, most existing solutions aggregate the electricity data of the entire user set, which cannot meet the fine-grained requirements of the control center. Therefore, this paper proposes a verifiable privacy-preserving multisubset dynamic billing data aggregation scheme. Firstly, we divide the electricity data into k consecutive subsets and set dynamic pricing rules, such as pricing within a certain range as q1and exceeding it as q2. Then, the smart meter encrypts the data using the Paillier cryptographic system with the corresponding subset of parameters and uploads them to the aggregation equipment. After the aggregation equipment completes the users’ data aggregation, the ciphertext C is sent to the control center. This process enables the control center to check data integrity and obtain the total number of people, electricity consumption, and costs for different ranges of electricity in a period of time at once, without the need to obtain data for individual users. Analysis and experiments show that this scheme can resist attacks from powerful adversaries on the transmission channel and has practicality and effectiveness.

Recently, researchers have leveraged the Denoising AutoEncoder (DAE) to reduce the noise in side-channel acquisitions (a.k.a. traces) that reduces the effectiveness of key recovery. Taking the${L}2$Loss (Mean Square Error, MSE) as the objective function of the DAE, it only aims to lessen the Euclidean Distance (ED) between the input and output, overlooking the Intra-Data Correlation (IDC) of the trace which includes the timing information. This paper proposes the Multi-Loss Regularized Denoising AutoEncoder (ML-DAE) framework to improve the generalization capability of the DAE. This framework consists of a shared DAE and Multiple Loss (ML) functions that aim to reduce the noise while preserving the excellent IDC of the output. During the training phase, to avoid issues of overfitting and a high number of training parameters, we pre-train the DAE using MSE and then initiate the ML-DAE which contains a multicore Partial Loss (PL) function with parameters transferred from the pre-trained DAE. During the testing phase, the outputs from the multicore PL are fused using an average pooling layer to yield the final predictions. The experiments on highly noisy datasets (XMEGA_ME, DPA_V2, and AES_GPU) and the masked dataset ASCAD demonstrate that ML-DAE achieves an SNR gain of at least four times, hence Deep-Learning based Side-Channel Attacks (DLSCAs) and Template Attacks (TA) with denoising pre-processing reduce of the number of traces needed to recover the key in the attack phase by more than 55%.

Threshold anonymous credentials enable users to acquire credentials in a decentralized manner while upholding their privacy. However, distributed network environments, such as electronic voting systems and federated identity management systems, have pressing needs for enhancing security, reducing reliance on fixed-group issuers, and achieving scalability. These requirements expose the significant constraints of existing threshold anonymous credential systems, which struggle to support dynamic threshold settings. This struggle leads to the necessity of system rewinding whenever an issuer is included or excluded. Moreover, the communication and computation complexities involved in showing credentials exhibit a linear relationship with the number of credentials possessed by each user. In this paper, we present a novel dynamic threshold anonymous credential system, named DTACB, to tackle the aforementioned challenges. DTACB enables the dynamic adjustment of thresholds, allowing issuer adjustments without rewinding the system. DTACB additionally supports batch-showing of credentials and proof of credential quantity values while preserving the user’s credentials collection remains undisclosed. We conduct rigorous security analysis and validate our efficiency claims via implementing and benchmarking. In particular, DTACB effectively reduces the cost of batch-proof verification to 3.78 ms, independent of the user’s proof size.

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices underRaspberry-Pi 4BandRedmi-K30 Pro, respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

With global economic integration, transnational trade plays an important role, and maritime transportation is one of the important means of freight transportation. It is of great significance to build a secure and efficient maritime transportation system (MTS). The introduction of Internet of things technology makes MTS more perfect. The IoT-enabled MTS is composed of marine terminals and on-board sensors, land-based data centers and base stations, as well as satellite networks. Many researchers have carried out significant work to aggregate data in MTS. However, because the terrestrial base stations cannot cover most of the sea area, the isolated maritime terminals, those who drive to the area without base station coverage, need the assistance of satellite networks to complete the contact with the data center. In this paper, we propose an attribute based secure data aggregation scheme for isolated IoT-enabled MTS. In the novel scheme, the constant attributes of a maritime terminal are utilized to generate its certification. In addition, on-board sensors are introduced in the system to help aggregate the status and surrounding environment of the maritime terminal. These monitoring data are encrypted by the sensors and transmitted to the data center for the trustworthiness evaluation of the isolated maritime terminal. Besides, the zero-knowledge proof is utilized to confirm the legitimacy of participating users. What's more, the security analysis and the simulation results show that the novel scheme is secure and efficient for IoT-enabled MTS.

Attribute-based signature (ABS) is a versatile and useful cryptogrammic technology. In an ABS scheme, every signer is distributed a signing secret key in term of her/his attributes, and endorses a message in relation to some signing policy fulfilled by the signer's attributes. The verifier checks that the signature is indeed endorsed by the signer whose attributes match with the signing policy. However, existing ABS schemes suffer from the issue of abusing signature and key exposure. To address the above issues, we provide a traceable and forward-secure attribute-based signature (TFS-ABS) scheme with constant-size supporting flexible threshold predicates. Furthermore, we prove that the presented TFS-ABS scheme is existential unforgeability against selective predicate attack under the standard model. We reduce the security for the provided scheme to$q$-Diffie-Hellman exponentiation assumption. The designed scheme can be used to alleviate the damage induced by key exposure and traces the real identity of signer by attribute authority (AA) when the signer occurs abusing behavior. Furthermore, the signature size in presented scheme keeps constant and is independent of the number of attributes. Experimental evaluations exhibit that the presented TFS-ABS scheme is efficient in term of the communication and computation overhead.

In the cloud computing context, group data sharing, which is highly convenient for people who intend to share data with multiple members of a group, has attracted extensive research attention. However, the question of how data security might be ensured in this scenario, particularly as regards the security of group session keys, still remains an open one. To support secure group data sharing in cloud computing, it is essential to design a key agreement protocol characterized by both high flexibility and efficiency. Accordingly, this paper proposes a key agreement protocol based on Latin Square to reinforce the security of group data sharing. This protocol supports an arbitrary number of members to participate in a group and make equal contributions to generating a common session key among themselves. Compared with previous works, both the communication and the computational complexity in this protocol are significantly reduced. In addition, the services of authentication, key confirmation and fault tolerance are provided, enabling the protocol to resist different types of attacks. The results of both theoretical and experimental analysis indicate that the proposed protocol can efficiently support secure cloud data sharing for large-scale groups.

In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data trans-mission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTnk protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.

Benefiting from the progress of Internet of Things (IoT) technology, medical devices, wearables, sensors, and users can be connected with each other to form an Internet of Medical Things (IoMT) ecosystem. IoMT improves efficiency, increases accuracy, and reduces the costs of the traditional healthcare system. However, since IoMT involves different entities and heterogeneous networks and carries a large amount of private information, it is a challenging task to ensure data security and protect privacy in the IoMT ecosystem. In this article, we focus on the issue of privacy-aware authentication between entities. We first propose a blockchain-assisted authentication framework for IoMT applications in the fog computing paradigm. Furthermore, we present two privacy-preserving authentication protocols based on elliptic curve cryptography (ECC) and physically unclonable functions (PUFs), respectively, in terms of the capacity of involved entities. Security analysis and performance evaluation demonstrate that compared with several previous protocols, the proposed protocols have competitive computation and communication costs while achieving expected security requirements.

Incompletely predictable vehicular ad hoc networks is a type of networks where vehicles move in a certain range or just in a particular tendency, which is very similar to some circumstances in reality. However, how to route in such type of networks more efficiently according to the node motion characteristics and related historical big data is still an open issue. In this paper, we propose a novel routing protocol named trustworthiness evaluation-based routing protocol (TERP). In our protocol, trustworthiness of each individual is calculated by the cloud depending on the attribute parameters uploaded by the corresponding vehicle. In addition, according to the trustworthiness provided by the cloud, vehicles in the network choose reliable forward nodes and complete the entire route. The analysis shows that our protocol can effectively improve the fairness of the trustworthiness judgement. In the simulation, our protocol has a good performance in terms of the packet delivery ratio, normalized routing overhead and average end-to-end delay.

With the development of cloud computing, the great amount of storage data requires safe and efficient data sharing. In multiparty storage data sharing, first, the confidentiality of shared data is ensured to achieve data privacy preservation. Second, the security of stored data is ensured. That is, when stored shared data are subject to frequent access operations, the address sequence or access pattern of data is hidden. Therefore, determining how to ensure the untraceability of stored data or efficient hide the data access pattern in sharing stored data is a challenge. By employing the proxy re-encryption algorithm and oblivious random access memory (ORAM), a privacy-preserving and untraceable scheme is proposed to support multiple users in sharing data in cloud computing. On the one hand, group members and a proxy use the key exchange phase to obtain keys and resist multiparty collusion if necessary. The ciphertext obtained according to the proxy re-encryption phase enables group members to implement access control and store data, thereby completing secure data sharing. On the other hand, this article realizes data untraceability and a hidden data access pattern through a one-way circular linked table in a binary tree (OCLT) and obfuscation operation. Additionally, based on the designed structure and pointer tuple, malicious users are identified and data tampering is prevented. The security analysis shows that the protocol designed in this article can meet the security requirements of proxy re-encryption and ORAM. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

The rapid development of low-power integrated circuits, wireless communication, intelligent sensors, and microelectronics has allowed the realization of wireless body area networks (WBANs), which can monitor patients’ vital body parameters remotely in real time to offer timely treatment. These vital body parameters are related to patients’ life and health; and these highly private data are subject to many security threats. To guarantee privacy, many secure communication protocols have been proposed. However, most of these protocols have a one-to-one structure in extra-body communication and cannot support multidisciplinary team (MDT). Hence, we propose a lightweight and certificateless multi-receiver secure data transmission protocol for WBANs to support MDT treatment in this article. In particular, a novel multi-receiver certificateless generalized signcryption (MR-CLGSC) scheme is proposed that can adaptively use only one algorithm to implement one of three cryptographic primitives: signature, encryption or signcryption. Then, a multi-receiver secure data transmission protocol based on the MR-CLGSC scheme with many security properties, such as data integrity and confidentiality, non-repudiation, anonymity, forward and backward secrecy, unlinkability and data freshness, is designed. Both security analysis and performance analysis show that the proposed protocol for WBANs is secure, efficient, and highly practical.

The Industrial Internet of Things (IIoT) that introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. These data are analyzed to improve the manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal ciphertext. Therefore, encrypted data sharing should be implemented to analyze the devices’ data. In this article, the cloud storage solution for sensors is considered. To achieve a secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication first. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with a less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.

The vehicular ad hoc network (VANET) has become a hot topic in recent years. With the development of VANETs, how to achieve secure and efficient machine learning in VANETs is an urgent problem to be solved. Besides, how to ensure that users obtain the accurate results of machine learning is also a challenge. Based on the homomorphic encryption and secure multiparty computing technology, a flexible and privacy-preserving collaborative filtering scheme is proposed to accomplish the personalized recommendation for users, which is based on users’ interests and locations. On the one hand, the data can be updated by users flexibly to ensure the freshness and accuracy of the dataset of interest. On the other hand, the weighted values of user interest can be safely sorted to improve the accuracy of collaborative filtering effectively. Moreover, a novel collaborative filtering algorithm based on the homomorphic encryption technology is designed, which can guarantee that the calculated decryption result by machine learning is the same as the plaintext. Note that the privacy of user data can be preserved during machine learning in this algorithm. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for collaborative filtering in cloud computing in VANETs.

Subscription-based services, where broadcasting companies make profits by providing subscription programs to users, have become a popular application of wireless broadcast systems. Driving by business interests and data security, companies usually encrypt content of programs with session keys, then only authorized users can access the contents of their subscribed programs, respectively. It is challenging to flexibly distribute and update the session key, we are therefore motivated to propose an identity-based many-to-many subscription scheme (IMS) with efficient key management. The IMS scheme possesses three novel advantages: First, it provides a whole set of subscription flow, from the selection of programs of interest to the generation and updating of keys. Second, it supports program updating and all subscription behaviors. Third, it employs a signature and authentication mechanism to stipulate that only authorized users can access the programs’ ciphertext. The theoretical analysis demonstrates that the proposed scheme is secure against chosen-ciphertext attacks (CCA) under the$ k$-bilinear Diffie-Hellman exponent (BDHE) problem, and has a decent performance in storage and computational overhead. The experimental analysis shows the total time cost of the proposed scheme is reduced more than 75.02 percent compared against the popular peer schemes under the condition of 2500 users.

As a promising technology in the development of human healthcare services, the wireless body area networks (WBANs) technology has attracted widespread attention in recent years from both industry and academia. However, due to the sensitiveness of the medical system and the capability limitation of the wearable devices, security, privacy, and efficiency of the healthcare services in WBANs are remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. In this article, we firstly provide a review and cryptanalysis on the state-of-the-art authentication scheme. In order to meet the challenges and address the drawbacks in previous works, we then propose a new efficient and anonymous authentication scheme for cloud based WBANs. Through the security analysis, we show that our scheme could overcome the weaknesses in previous schemes and meet all the security requirements. Besides, we show the advantages of the proposed scheme through performance evaluation in terms of functionality features, computation overhead, communication overhead and storage overhead, which shows our scheme is more appropriate for practical applications on healthcare services.

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and a high level of medical service. Specifically, to make sure those people with a high incidence of chronic diseases, including hypertension, diabetes, and cardiovascular diseases, are taking care of which further reduces social costs. Thus, both industry and academia in recent years pay widely attention to WBANs technology. After years of research, security, privacy, and efficiency of the healthcare services in WBANs have remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. To ensure the security and privacy of patients’ sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs.

Summary Smart home networks (SHNets) are based on the Internet of Things (IoT) and provide users with a highly intelligent home life. In recent years, SHNets have become more and more popular. In SHNets, a large amount of data interactions are needed. These data related to the privacy of users may be subject to attacks such as eavesdropping attacks, replay attacks, and mobile phone stolen attacks. Privacy protection and communication security in smart home networks have been widely studied by many scholars. However, most of the existing user authentication in current researches focus on identity‐based authentication, where the encryption protocols are divided into two steps: the signing step and the encryption step. In this paper, a certificateless signcryption scheme for SHNets is proposed which can provide biometric‐based user identity authentication. The security and performance analyses show that our scheme is efficient and secure.

Internet of Things (IoT) has been introduced into smart grids, which has achieved great improvement. The statistics of power consumption is one of the important functions but could lead to the leakage of user daily behaviour. Researchers have put efforts into secure data aggregation protocols to avoid such potential risk. However, only a few schemes have considered the dynamic unit price of electricity, and no schemes have been designed for calculating the power consumption of every appliance in a specific area. This paper proposes a novel appliance-based data aggregation scheme (ABDAS) for bill generation and demand management in smart grids. In the proposed scheme, chameleon hash function (CHF) is utilised to facilitate the extraction of aggregated data due to the characteristic of collision controllability. Furthermore, indistinguishability obfuscation (IO) is utilised to keep the chameleon hash value secret and decrease the overhead of the trusted third party. The fog nodes (FNs) in our scheme are responsible for the calculation of aggregation with its powerful computing and storage capabilities. The security analysis shows that our scheme satisfies IND-CPA and multiple security goals. Additionally, the performance evaluation indicates that the computational overhead of our scheme is lower than that of existing schemes.

The delay of vehicle emergency has led to many serious consequences. A series of studies has been carried out in the field of information security in vehicularad hocnetworks (VANETs). However, open issues such as the authentication of emergency vehicle (EV) avoidance are remaining unsolved. In this article, we propose a novel lightweight authentication protocol to avoid EVs in VANETs. In our protocol, after completing the first mutual authentication with the nearest roadside unit (RSU), EV can complete the mutual identity authentication with the subsequent RSUs without repeating cumbersome calculations. Additionally, EV is required to verify the legitimacy of the driver’s identity when starting to avoid some illegal driving behavior. The RSUs will broadcast avoidance information to ordinary vehicles in their jurisdiction to remind them to clear a temporary emergency lane for EVs in advance. With temporary emergency lanes, emergent mission delays due to traffic congestion could be reduced. The security analysis and efficient analysis prove that our protocol is practical and efficient against attacks, such as impersonation attacks, device theft attacks, reputation attacks, etc.

With the development of the Internet of Things and the demand for telemedicine, the smart healthcare system has attracted much attention in recent years. As a platform for medical data interaction, the smart healthcare system is demanded to ensure the privacy of both the receiver and the sender, as well as the security of data transmission. In this paper, we propose a privacy-preserving data transmission scheme where both secure ciphertext conversion and malicious users identification are supported. In particular, the OT m n protocol is introduced to guarantee the two-way privacy of communication parties. Meanwhile, we adopt proxy reencryption algorithm to support secure ciphertext conversion so as to ensure the confidentiality of data in many-to-many communication pattern. In addition, by taking advantage of the concept of blockchain technology, a novel OT m n protocol is proposed to prevent data from being tampered with and effectively identify malicious users. Theoretical and experimental analyses indicate that the proposed scheme is practical for smart healthcare with high security and efficiency.

Intelligent transportation systems (ITS) have always been an important application of Internet of Things (IoT). Today, big data and cloud computing have further promoted the construction and development of ITS. At the same time, the development of blockchain has also brought new features and convenience to ITS. However, due to the endless emergence of increasingly advanced types of attacks, the security of blockchain-based ITS needs more attention from industry and academia. In this paper, we focus on exploring the primitives in cryptography to guarantee the security of blockchain-based ITS. In particular, the authentication, encryption, and key management schemes in cryptography are discussed. Furthermore, we propose two methods for achieving the threshold key management in blockchain-based ITS. The proposed threshold key management scheme (with threshold t ) enables various stakeholders to recover a secret if the number of participated stakeholders is at least t . It should be noted that the proposed threshold key management scheme is efficient and secure for multiple users in blockchain-based ITS, especially for the data-sharing scenario.

With the fast growing demands for the big data, we need to manage and store the big data in the cloud. Since the cloud is not fully trusted and it can be accessed by any users, the data in the cloud may face threats. In this paper, we propose a secure authentication protocol for cloud big data with a hierarchical attribute authorization structure. Our proposed protocol resorts to the tree-based signature to significantly improve the security of attribute authorization. To satisfy the big data requirements, we extend the proposed authentication protocol to support multiple levels in the hierarchical attribute authorization structure. Security analysis shows that our protocol can resist the forgery attack and replay attack. In addition, our protocol can preserve the entities privacy. Comparing with the previous studies, we can show that our protocol has lower computational and communication overhead.

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based trustworthiness evaluation scheme for privilege management in VANETs is proposed in this article. In the proposed scheme, to ensure the result of trustworthiness is valid, mutual authentication with conditional anonymity between the evaluator and the vehicle to be evaluated is first employed. Then, based on the vehicle's behavioral big data, the trustworthiness of each vehicle is evaluated by utilizing the fuzzy theory. Note that the privilege of a vehicle and the reliability of the vehicle's messages are determined by its trustworthiness. Moreover, the mobility of vehicles is also considered in this article, since the location of a vehicle is not constant and the monitoring area of an road side unit is limited. The results of theoretical and experimental analyses demonstrate that the proposed scheme performs well in terms of security and efficiency.

The rapid development of the Internet of Things (IoTs), 5 G and artificial intelligence (AI) technology have been dramatically incentivizing the advancement of Internet of Medical Things (IoMT) in recent years. Profile matching technology can be used to realize the sharing of medical information between patients by matching similar symptom attributes. However, the symptom attributes are associated with patients' sensitive information such as gender, age, physiological data, and other personal health information, thus the privacy of patients will be revealed during the matching process in the IoMT. To solve the problem, this paper proposes a verifiable private set intersection scheme to achieve fine-grained profile matching. On the one hand, the privacy data of patients can be divided by multi-tag to implement fine-grained operations. On the other hand, re-encryption technique is utilized to protect the privacy of patients. In addition, the cloud server may violate the scheme, thus a verifiable mechanism is leveraged to check the correctness of computation. The analysis of security indicates that our proposed scheme can resist the untrusted cloud server and the performance simulation demonstrates that our scheme improves efficiency by reducing the use of bilinear pairs.

As cloud storage has been widely adopted in various applications, how to protect data privacy while allowing efficient data search and retrieval in a distributed environment remains a challenging research problem. Existing searchable encryption schemes are still inadequate on desired functionality and security/privacy perspectives. Specifically, supporting multi-keyword search under the multi-user setting, hiding search pattern and access pattern, and resisting keyword guessing attacks (KGA) are the most challenging tasks. In this article, we present a new searchable encryption scheme that addresses the above problems simultaneously, which makes it practical to be adopted in distributed systems. It not only enables multi-keyword search over encrypted data under a multi-writer/multi-reader setting but also guarantees the data and search pattern privacy. To prevent KGA, our scheme adopts a multi-server architecture, which accelerates search response, shares the workload, and lowers the key leakage risk by allowing only authorized servers to jointly test whether a search token matches a stored ciphertext. A novel subset decision mechanism is also designed as the core technique underlying our scheme and can be further used in applications other than keyword search. Finally, we prove the security and evaluate the computational and communication efficiency of our scheme to demonstrate its practicality.

Summary Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi‐server‐aided PoSW (ms‐PoSW) scheme for securing client‐side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms‐PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes.

With the development of wireless communication and the pervasive Internet of Things, vehicular ad hoc networks (VANETs) have attracted much attention in recent years. As a platform for vehicular communication and management, VANETs require the guarantee of security and efficiency for the resources constrained and unreliable networks. In this article, a data-sharing scheme is proposed for VANETs where both efficient key updating and dynamic property of VANETs are supported. In particular, the symmetric balanced incomplete block design (SBIBD) in combinatorics is introduced to ensure secure and efficient VANETs while the concept of indistinguishability obfuscation is employed to support efficient key updating. In order to simultaneously satisfy the resources constrained and real-time requirements of VANETs, both serial and parallel construction of the SBIBD is implemented by the shift register. Taking advantage of this efficient sequential logical circuit, we eliminate the complicated construction of the SBIBD, thereby, making it effective for the resource-constrained environment. Theoretical and experimental analyses indicate that the proposed scheme is practical for VANETs with high security and efficiency.

With the rapid development of cloud services, the resources-constrained enterprises and individuals can outsource the huge sensitive data into the Cloud Service Providers (CSPs) who fully control the data physically. Since CSPs are not fully trusted, it is essential to protect the integrity and confidentiality of users' data. Plenty of researchers have devoted considerable attention to solve this issue in the last decade such as various PDP and POR schemes. In this paper, we propose an algebraic signature-based data integrity auditing scheme that ensures the cloud data integrity and confidentiality with batch auditing. Moreover, one advantage of the scheme is that it can also support data dynamics by using only one cloud server. The security analysis shows that our construction can achieve the desired security properties. We also provide the simulation results of the dynamic operations on different numbers of data blocks and sub-blocks, which show that our scheme is efficient for real-world applications.

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.

Summary Vehicular ad hoc networks (VANETs) are a type of network, which have caused widespread concern. Researchers have done many research on security and reliability of information transmission in VANETs. However, the secure transmission of emergent information, such as accident information, remains an open issue. The vehicle‐to‐vehicle authentication scheme for traffic accident information transmission not only needs to ensure the orderly and secure transmission of information but also to ensure efficient and rapid transmission. In this paper, we propose a novel scheme for VANETs named neighborhood trustworthiness‐based vehicle‐to‐vehicle authentication scheme (NTVAS). NTVAS makes reasonable utilization of cloud computing technology to evaluate the trustworthiness of vehicles for message delivery. One way vehicle‐to‐vehicle authentication ensures the efficiency of the scheme. Additionally, the concept of accident location neighborhood trustworthiness has also been innovatively presented for further transmission of accident information. The security and performance analysis indicates that our scheme is secure and efficient with low computational cost.

Data sharing in cloud computing enables multiple participants to freely share the group data, which improves the efficiency of work in cooperative environments and has widespread potential applications. However, how to ensure the security of data sharing within a group and how to efficiently share the outsourced data in a group manner are formidable challenges. Note that key agreement protocols have played a very important role in secure and efficient group data sharing in cloud computing. In this paper, by taking advantage of the symmetric balanced incomplete block design (SBIBD), we present a novel block design-based key agreement protocol that supports multiple participants, which can flexibly extend the number of participants in a cloud environment according to the structure of the block design. Based on the proposed group data sharing model, we present general formulas for generating the common conference key IC for multiple participants. Note that by benefiting from the (v, k + 1, 1)-block design, the computational complexity of the proposed protocol linearly increases with the number of participants and the communication complexity is greatly reduced. In addition, the fault tolerance property of our protocol enables the group data sharing in cloud computing to withstand different key attacks, which is similar to Yi's protocol.

The concept of the Social Internet of Things (SIoT) can be viewed as the integration of prevailing social networking and the Internet of Things, which is making inroads into the daily operation of many industries. Smart grids, which are cost-effective and environmentally friendly applications, are a promising field of the SIoT. However, security and privacy concerns are the dark aspects of smart grids. The goal of this paper is to address the security and privacy issues in the vehicle-togrid (V2G) networks with the intention of promoting a more extensive deployment of V2G networks for smart grids. Driven by this motivation, in this paper, we propose a robust key agreement protocol that can achieve mutual authentication without exposing the real identities of users. Efficiency is also a major concern in resource-constrained environments. By leveraging only hash functions and bitwise exclusive-OR operations, the proposed protocol is highly efficient compared with pairing-based protocols. In addition, we define a formal security model for our privacy-preserving key agreement protocol for V2G networks. Using this model, a formal security analysis shows that the proposed protocol is secure. Moreover, an informal security analysis demonstrates that our protocol can withstand different types of attacks.

Nowadays, Software Defined Network (SDN) develops rapidly for its novel structure which separates the control plane and the data plane of network devices. Many researchers devoted themselves to the study of such a special network. However, some limitations restrict the development of SDN. On the one hand, the single controller in the conventional model bears all threats, and the corruption of it will result in network paralysis. On the other hand, the data will be increasing more in SDN switches in the data plane, while the storage space of these switches is limited. In order to solve the mentioned issues, we propose two corresponding protocols in this paper. Specifically, one is an anonymous protocol in the control plane, and the other is a verifiable outsourcing protocol in the data plane. The evaluation indicates that our protocol is correct, secure, and efficient.

Internet of Things (IoT) is a research field that has been continuously developed and innovated in recent years and is also an important driving force for the improvement of people’s life in the future. There are lots of scenarios in IoT where we need to collaborate through devices to complete tasks; that is, a device sends data to other devices, and other devices operate on the aid of the data. These transmitted data are often users’ privacy data, such as medical data and grid data. We propose an instant encrypted transmission based security scheme for such scenarios in IoT. The analysis in this paper indicates that our scheme can guarantee the security of users’ data while ensuring rapid transmission and acquisition of instant IoT data.

Cyberspace has become the most popular carrier of information exchange in every corner of our life, which is beneficial for our life in almost all aspects. With the continuous development of science and technology, especially the quantum computer, cyberspace security has become the most critical problem for the Internet in near future. In this paper, we focus on analyzing characteristics of the quantum cryptography and exploring of the advantages of it in the future Internet. It is worth noting that we analyze the quantum key distribution (QKD) protocol in the noise-free channel. Moreover, in order to simulate real situations in the future Internet, we also search the QKD protocol in the noisy channel. The results reflect the unconditional security of quantum cryptography theoretically, which is suitable for the Internet as ever-increasing challenges are inevitable in the future.

Group data sharing in cloud environments has become a hot topic in recent decades. With the popularity of cloud computing, how to achieve secure and efficient data sharing in cloud environments is an urgent problem to be solved. In addition, how to achieve both anonymity and traceability is also a challenge in the cloud for data sharing. This paper focuses on enabling data sharing and storage for the same group in the cloud with high security and efficiency in an anonymous manner. By leveraging the key agreement and the group signature, a novel traceable group data sharing scheme is proposed to support anonymous multiple users in public clouds. On the one hand, group members can communicate anonymously with respect to the group signature, and the real identities of members can be traced if necessary. On the other hand, a common conference key is derived based on the key agreement to enable group members to share and store their data securely. Note that a symmetric balanced incomplete block design is utilized for key generation, which substantially reduces the burden on members to derive a common conference key. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

Machine-to-machine (M2M) communication, an automated communications technology for the equipment or devices, holds great promise in every corner of the modern society, such as civil transportation, smart homes, smart grids, and industrial automation. The M2M technology is still in its infancy, and further development and deployment of M2M systems hinges on establishing an efficient and secure information management system with a satisfactory security level. In this paper, we extend the idea of (t, n) secret sharing for information transmission in M2M with high security and efficiency. Specifically, a secret is divided into 2k shares, and then, transmitted through 2k node-disjoint paths constructed by the Latin square. Note that in our scheme, the secret key is simultaneously transmitted along with the encrypted message through these 2k paths from the source node to the destination node, which greatly improves the efficiency of point-to-point communications in M2M systems. Furthermore, owing to the properties of (t, n) secret sharing, the security of M2M communications is guaranteed. In addition, to avoid dishonest participants, verifiable secret sharing is supported in the proposed scheme. Sufficient theoretical proof and performance analysis demonstrate that our scheme is secure and efficient for M2M communications.

Certificate-based cryptography is a novel cryptographic primitive that has many attractive merits. It solves the certificate revocation problem in conventional public key cryptography and overcomes the key-escrow problem in identity-based cryptography. Recently, Li et al. presented a certificate-based key-insulated signature (CBKIS) scheme in the standard model. However, their scheme suffers from a security vulnerability caused by the malicious certification authority (CA) attack. Our cryptanalysis shows that a malicious CA is able to break its unforgeability by implanting some trapdoors in the public system parameters. To remedy the security weakness in Li et al.’s scheme, we put forward an improved CBKIS scheme. Under the complexity assumption of the square computational Diffie–Hellman problem, the improved scheme is proven to be existentially unforgeable in the standard model. Compared with the original CBKIS scheme proposed by Li et al., it enjoys better performance while offering stronger security guarantee as it can resist the malicious CA attack.

Outsourced decryption ABE system largely reduces the computation cost for users who intend to access the encrypted files stored in cloud. However, the correctness of the transformation ciphertext cannot be guaranteed because the user does not have the original ciphertext. Lai et al. provided an ABE scheme with verifiable outsourced decryption which helps the user to check whether the transformation done by the cloud is correct. In order to improve the computation performance and reduce communication overhead, we propose a new verifiable outsourcing scheme with constant ciphertext length. To be specific, our scheme achieves the following goals. (1) Our scheme is verifiable which ensures that the user efficiently checks whether the transformation is done correctly by the CSP. (2) The size of ciphertext and the number of expensive pairing operations are constant, which do not grow with the complexity of the access structure. (3) The access structure in our scheme is AND gates on multivalued attributes and we prove our scheme is verifiable and it is secure against selectively chosen-plaintext attack in the standard model. (4) We give some performance analysis which indicates that our scheme is adaptable for various limited bandwidth and computation-constrained devices, such as mobile phone.

With the rapid development of cloud computing, cloud storage has been accepted by an increasing number of organizations and individuals, therein serving as a convenient and on-demand outsourcing application. However, upon losing local control of data, it becomes an urgent need for users to verify whether cloud service providers have stored their data securely. Hence, many researchers have devoted themselves to the design of auditing protocols directed at outsourced data. In this paper, we propose an efficient public auditing protocol with global and sampling blockless verification as well as batch auditing, where data dynamics are substantially more efficiently supported than is the case with the state of the art. Note that, the novel dynamic structure in our protocol consists of a doubly linked info table and a location array. Moreover, with such a structure, computational and communication overheads can be reduced substantially. Security analysis indicates that our protocol can achieve the desired properties. Moreover, numerical analysis and real-world experimental results demonstrate that the proposed protocol achieves a given efficiency in practice.

Efficient power management in smart grids requires obtaining power consumption data from each resident. However, data concerning user's electricity consumption might reveal sensitive information, such as living habits and lifestyles. In order to solve this problem, this paper proposes a privacy-preserving cube-data aggregation scheme for electricity consumption. In our scheme, a data item is described as a multi-dimensional data structure (l-dimensional), and users form and live in multiple residential areas (m areas, and at most n users in each area). Based on Horner's Rule, for each user, we construct a user-level polynomial to store dimensional values in a single data space by using the first Horner parameter. After embedding the second Horner parameter into the polynomial, the polynomial is hidden by using Paillier cryptosystem. By aggregating data from m areas, we hide the area-level polynomial into the final output. Moreover, we propose a batch verification scheme in multi-dimensional data to reduce authentication cost. Finally, our analysis shows that the proposed scheme is efficient in terms of computation and communication costs, suitable for massive user groups, and supports the flexible and rapid growth of residential scales in smart grids.

Abstract The continuous auxiliary inputs leakage is more strong side‐channel attacks. In this article, we first propose a continuous auxiliary inputs leakage model for the hierarchical attribute‐based encryption scheme. Under the security model, an adversary has ability to gain partial updated master keys and updated secret keys continually by certain leakage attacks. Moreover, a resilient‐leakage hierarchical attribute‐based encryption scheme is constructed. The security proof for this scheme is provided under the standard model. Furthermore, we give the performance comparison between our scheme and relevant scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large‐scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel‐based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi‐objective algorithm called time and space optimized NSGA‐II (TS‐NSGA‐II). Experiment results have shown that compared with the original load‐balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6%improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Recently, Chen et al. proposed a certificateless aggregate signature scheme with efficient verification. They claimed that their scheme could resist attacks of Type I adversary and Type II adversary. Unfortunately, we present a universal attack to demonstrate that their scheme cannot provide unforgeability. The adversary in the proposed attack can forge a legal signature on any message without any users' secret information. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A program obfuscator is a compiling algorithm that takes a program/circuit as input and generates a new garbled circuit to implement the same functionality as before while obtaining hard‐to‐understand in some sense, that is, infeasible to learn information from the garbled circuit. In order to obtain a practical application, an obfuscation should satisfy equivalent in functionality, polynomial slowdown in efficiency, and virtual black‐box in security. In this paper, we model a stronger cryptographic obfuscation that does not only achieves the obfuscated circuit obscurity but also supports input re‐key privacy. In order to implement the re‐encryption obfuscation, we at first propose a key‐privacy two‐level encryption mechanism that implicitly supports the transformation from level‐2 ciphertext into level‐1 one, which provides an efficient method to re‐encrypt the ciphertext without explicitly decryption procedure. Under the mechanism of two‐level encryption and function of re‐encryption, we construct an obfuscation of re‐encryption that takes as input a probabilistic (keys) circuit and outputs a transformed circuit. We also give the proof that the obfuscator achieves the average‐case security for the circuit family under the extended DBDH assumption and Decisional Linear assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd.

Summary In recent years, many energy‐efficient algorithms and data dissemination protocols have been proposed for wireless sensor networks (WSNs). Because sensor nodes close to sink node have more traffic loads, they will quickly deplete their limited energy in practical implementation, and it will finally lead to energy hole and network partition problem. Adding sink mobility into sensor networks can bring in new opportunities to improve energy efficiency for WSNs. In this paper, we proposed our multiple mobile sink‐based routing algorithm for data dissemination to improve WSNs performance. Multiple mobile sinks will be utilized to collect the interested raw data. They will move back and forth along predetermined paths; one of which is the diameter of the circle, and the other two are fixed on arc lines. Mobile sinks will sojourn at some fixed points to collect raw data from relevant areas. Extensive simulation results show that our proposed algorithm can efficiently mitigate the hot spots problem and prolong the network lifetime of WSNs. Copyright © 2014 John Wiley & Sons, Ltd.