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 uncrewed 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.

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.

The widespread adoption of voice control systems (VCSs) in smart devices and their growing integration into daily life highlight the urgent need to address their security challenges. Recent studies have revealed a range of vulnerabilities in VCSs, which threaten user privacy and safety. Despite these findings, there remains a notable absence of a comprehensive and systematic review that thoroughly examines these vulnerabilities and the corresponding defense strategies. This gap hinders VCS designers from fully understanding and addressing the security risks inherent in these systems. To bridge this gap, our research introduces a hierarchical model for VCS, providing a structured and innovative framework for organizing and analyzing existing literature. We categorize attacks based on their underlying technical principles and conduct an in-depth analysis of various aspects, such as attack methodologies, targets, vectors, and behaviors. Furthermore, we synthesize and critically evaluate current defense mechanisms, offering practical recommendations to strengthen VCS security. Analyzing VCS security from a hierarchical perspective, this work provides designers with the tools to effectively identify and mitigate potential threats while establishing a foundation for future research and advancements in VCS security.

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.

Internet of Things (IoT)-based smart grid systems require the implementation of diverse functionalities, data privacy protection, and lightweight solutions for terminal devices. In addition, data aggregation plays a critical role within IoTbased 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. First, homomorphic encryption can hinder the efficient processing capabilities of electric utilities, impacting the critical time required to meet specific functional requirements. Second, 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 Horner's rule aggregation method to aggregate users' personal data before transmitting it to the control center (CC), 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 CC 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 10(2) and greater.

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.

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.

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 of Proxy-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.

Threshold signatures, as a cornerstone for establishing decentralized trust, have been extensively researched in recent years. However, among these studies, few threshold signature schemes can simultaneously satisfy the critical properties of accountability, privacy-preserving, and key protection for BLS based internet of vehicles (IoV) systems. In this paper, we propose a BLS-based threshold accountable private signature with proactive refresh, named BLS-TAPS-PR, a novel accountable threshold signature protocol that bridges this gap. BLS-TAPS-PR enables privacy-preserving signing with hidden thresholds and signer sets, while ensuring deterministic accountability of malicious signers and enhancing key security through a proactive refresh mechanism. A key technical contribution is the integration of an adaptive symmetric balanced incomplete block design (SBIBD) algorithm to optimize the distributed key generation (DKG) phase. By partitioning nodes into logical blocks, we reduce the communication complexity and peak bandwidth consumption from O(n) to O(√n), significantly enhancing scalability for large scale networks. We formally prove the security of BLS-TAPS PR under the SXDH assumption in the random oracle model. Extensive evaluations using the NS-3 simulator in vehicular ad Hoc networks (VANETs) scenarios demonstrate that our protocol yields over 60% reduction in bandwidth consumption compared to EdDSA-based alternatives.

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.

Electronic Health Record (EHR) has improved medical data management efficiency through cloud-based storage and sharing. However, storing sensitive EHR data in third-party clouds introduces serious security and privacy risks. Registered attributebased encryption (RABE), building on the advantages of traditional attribute-based encryption, fundamentally addresses the key escrow problem by radically changing the trust model. As an emerging cryptographic primitive, RABE provides a promising foundation for key-escrow-free secure data sharing. Nevertheless, the lack of dynamic permission updates and high decryption overhead hinder the practical adoption of existing RABE schemes. Motivated by these challenges, we first propose an RABE-based access control scheme for medical cloud environments. The scheme supports dynamic ciphertext updates to accommodate permission changes without frequent re-encryption and enables outsourced decryption to reduce user-side computational overhead. On this basis, we further propose a scheme with consistency verification to ensure the correctness of updated ciphertexts. Security analysis and experimental results demonstrate that the proposed schemes effectively preserve data privacy, enhance access control flexibility, and show practical potential for secure data sharing in real-world medical cloud environments.

Web service platforms must not only ensure the computability of the encrypted data but also support the traceability of data leakage when sharing high-dimensional sensitive data. Functional encryption (FE) enables designated functional operations on ciphertexts without revealing the original plaintext information, making it a key technology for privacy-preserving computation in Web scenarios. However, most existing FE schemes are limited by fixed vector dimensions or reliance on indistinguishability obfuscation ($i\mathcal {O}$), which hinders the simultaneous achievement of unbounded inner product evaluation and effective tracing of malicious users. To overcome these limitations, we present a novel framework called traceable registered functional encryption for unbounded inner product (TRFE-UIP). Our framework eliminates the need for preset dimensional bounds and supports unbounded inner product operations without $i\mathcal {O}$. Furthermore, we introduce a public black-box tracing mechanism that can accurately identify malicious leakers without requiring decryption. In our framework, each client generates their own public/secret key pair locally and completes registration through a transparent key curator (KC). The KC only provides a public service and is not entrusted with any secret information, thus effectively mitigating key escrow risks. To instantiate the framework, we construct a concrete scheme based on bilinear pairings and prove that it achieves very selective simulation-based security and public black-box traceability in the standard model. Experimental results show that for the message vector dimension $n$ up to 100 and the client registration number $L$ reaching 100, the total communication overhead of our TRFE-UIP scheme exhibits a near-linear growth with $n$ and $L$. Moreover, its total computational cost is reduced by $96.4\%$ compared with that of the current mainstream scheme. Therefore, our scheme is highly suitable for Web application scenarios involving large-scale user access and high-dimensional vector interactions.

With the rapid advancement of the 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 multitenant environments make it necessary for the system to quickly adapt to policy changes to ensure secure data sharing. Attribute-based encryption (ABE) 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 article, 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.

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 communication systems, embedded devices rely on encryption techniques to ensure the security of data transmission. The leakage of side-channel information during the encryption process provides potential analysis objects for attackers, which may lead to the exposure of encryption keys. However, in practical scenarios, due to the limited control that attackers have over the target devices, they often face a small sample problem, which poses challenges for profiling-based side-channel analysis (SCA). To address this issue, this paper proposes a novel cross-leakage profiling side-channel analysis method, denoted as CLeP, aimed at revealing the potential leakage threats that communication devices still face with limited data samples. We evaluate this method using the AES-128 algorithm, considering that a captured trace consists of 10 rounds of encryption, with each round containing 4 operations, and each operation involving 16 bytes. Notably, the leakage from the round key addition and byte substitution operations is the most significant. Therefore, we decompose a single trace into 336 operation values to derive the corresponding leakage features for each operation. Additionally, we introduce an autoencoder (AE) and a maximum mean discrepancy (MMD) algorithm to constrain the differences between the two types of operations. Our experimental results demonstrate that the CLeP method significantly enhances attack performance in small sample scenarios.

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.

Blockchain provides a decentralized and tamper-resistant foundation for trustworthy data management and sharing in zero-trust environments. However, the immutability of conventional blockchains conflicts with regulatory requirements such as the GDPR's "right to be forgotten," while existing solutions often suffer from weak policy privacy protection capabilities and limited support for efficient revocation and traceability. These limitations hinder secure and flexible data management in multi-party collaboration. To address these challenges, we propose a privacy-preserving redactable blockchain scheme that supports policy hiding and revocation for secure data sharing. The proposed scheme integrates a hierarchical blinding factor to realize dynamic expiration of chameleon hash trapdoors, enabling efficient batch revocation, parallel multi-segment modification, and resilience against trapdoor leakage. Furthermore, ciphertext-policy attribute-based encryption (CP-ABE) is extended with lightweight inner-product encoding to enforce fine-grained authorization while concealing access policies. A revocation and traceability framework further guarantees immediate permission invalidation, tamper-evident auditing, and consistent synchronization between on-chain and off-chain data. Experimental results demonstrate that the proposed scheme significantly outperforms existing redactable blockchain approaches, achieving up to 7.7 & times; higher efficiency and reducing computational cost by 66.8%.

With the rapid development of Low-Altitude Intelligent Internet of Things (LAI-IoT), uncrewed aerial vehicles (UAVs) have become critical assistants for urban traffic management systems to acquire real-time data. They are widely deployed in key scenarios such as traffic accident scene image collection and dynamic traffic flow monitoring, providing essential data support for traffic scheduling and emergency response. Beyond the risk of raw data leakage, derivative information (including dataset cardinality, retrieval requests, and UAV communication trajectories) may also disclose sensitive situational information. If the urban planning brain repeatedly makes large requests for a specific intersection, the server may infer an upcoming major project, while disclosing the UAV provider's total data volume reveals its operational scale and costs, disadvantaging it in future negotiations. Existing LAI-IoT communication schemes primarily focus on protecting users' direct privacy, often overlooking the privacy of derivative data (e.g., dataset cardinality and communication trajectories), which can be exploited and misused by AGI models. Therefore, a privacy-preserving collaborative communication protocol for distributed UAVs based on a hierarchical key encapsulation mechanism is proposed. The protocol enables UAV datasets to accurately identify accident scenes while preserving multiple dimensions of privacy, including dataset cardinality at the UAV side, retrieval tag sets and their cardinalities at base stations, and UAV communication trajectories, without compromising communication efficiency. Theoretical analysis and experimental evaluation demonstrate that the proposed protocol maintains strong scalability and practicality while significantly reducing the success rate of cardinality and trajectory inference attacks, thereby enhancing the security of data communications in LAI-IoT scenarios.

With the explosive growth of data, mobile devices are generally set to periodically transmit stored data to cloud storage servers to free up local memory. Therefore, it is inevitable to face issues of data authentication and access control. Attribute-based signature (ABS) is a significant cryptographic technology, which has widespread attention for its data authentication and anonymous access control. In ABS, once signing keys are leaked, an adversary who obtains signing keys can generate a valid signature. In addition, the signature contains some sensitive data, which is not made public and needs to be deleted or substituted. To address above problems, an attribute-based sanitizable signature with key-exposure resistance (ABSS-KER) scheme is proposed. Besides, we prove that the proposed ABSS-KER scheme is existential unforgeability against chosen-policy in the standard model, which reduces to the $q$-Diffie-Hellman exponentiation assumption. When a signer needs to hide sensitive data of signed message, he/she utilizes a sanitizer to modify the designated signing data. The sanitizer is a semi-trusted party and is designated by the signer. Besides, the proposed scheme is able to mitigate the harm caused by key exposure. Meanwhile, the size of the signature maintains constant in the proposed scheme, regardless of the quantity of attributes. The results of experiment demonstrates the superiority of the proposed ABSS-KER scheme in terms of computation overhead and communication overhead.

With the rapid development of intelligent transportation systems, vehicular networks (VNs) have become a key component for acquiring and transmitting data regarding vehicle movement and environmental conditions. Considering the privacy and security issues within VNs, Federated Learning (FL), as a distributed machine learning approach, offers a powerful tool that utilizes the onboard central processing units (CPUs) and local datasets of mobile Connected Vehicles (CVs) to train a global model. However, most existing FL implementations rely on synchronous training to ensure favorable convergence performance, but the mobility of CVs often leads to communication instability, which may result in significant straggler issues. Furthermore, CVs frequently leave the coverage area of the roadside unit (RSU) before successfully uploading their local models, thereby reducing the accuracy of the global model. To address the above problems, in this paper, we propose a communication-efficient semi-asynchronous FL (ESAFL) scheme to fit to VNs and enhance learning performance. Specifically, the RSU clusters CVs into different network layers according to their arrival order in each epoch and mitigates the straggler effect in FL by adjusting the participation frequency of CVs in various layers. Afterward, an Age of Information aggregation strategy is employed to balance the contributions of models transmitted by CVs with varying delays. Exploiting the MNIST and CIFAR10 datasets in simulations, we show that our proposed ESAFL achieves significant improvements in both the accuracy of the model and the convergence speed, indicating a more effective approach to FL in dynamic and heterogeneous environments.