Homomorphic aggregate signature (HAS) is a linearly homomorphic signature (LHS) for multiple users, which can be applied for a variety of purposes, such as multi‐source network coding and sensor data aggregation. In order to design an efficient postquantum secure HAS scheme, we borrow the idea of the lattice‐based LHS scheme over binary field in the single‐user case, and develop it into a new lattice‐based HAS scheme in this paper. The security of the proposed scheme is proved by showing a reduction to the single‐user case and the signature length remains invariant. Compared with the existing lattice‐based homomorphic aggregate signature scheme, our new scheme enjoys shorter signature length and high efficiency.

The blockchain‐based management system has been regarded as a novel way to improve the efficiency and safety of Vehicular Ad Hoc Networks (VANETs). A blockchain‐based scheme’s performance depends on blockchain nodes’ computing power composed from the road‐side unit (RSU). However, the throughput of blockchain‐based application in VANETs is limited by the network bandwidth. A single blockchain cannot record large‐scale VANETs’ data. In this paper, we design an atomic cross‐chain swap‐based management system (ACSMS) to boost the scalability of blockchain‐based application in VANETs. The blockchain‐based public‐key encryption with keyword search is further introduced to protect user privacy. The analysis shows that ACSMS achieves cross‐chain swap without loss of CAV security privacy. The simulation results show that our method can realize multiple blockchain‐based applications in VANETs.

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. To address this gap in trust, explainability has become a key research focus. This paper provides a systematic review of explainable deepfake detection methods, categorizing them into three main approaches: forensic analysis, which identifies physical or algorithmic manipulation traces; model-centric methods, which enhance transparency through post hoc explanations or pre-designed processes; and multimodal and natural language explanations, which translate results into human-understandable reports. The paper also examines evaluation frameworks, datasets, and current challenges, underscoring the necessity for trustworthy, reliable, and interpretable detection technologies in combating digital misinformation.

Authentication is an important security issue for multi-access edge computing (MEC). To restrict user access from untrusted devices, Bring Your Own Device (BYOD) policy has been proposed to authenticate users and devices simultaneously. However, when integrating BYOD policy into MEC authentication to improve security, issues of efficient binding and user-device conditional anonymity have not been well supported. To address these issues, we propose Bring Your Device Group (BYDG) policy by constructing efficient and privacy-preserving user-device authentication. Our core idea is to use key sequences generated by PUFs-based key derivation functions (KDFs) to not only construct efficient binding relationships, but also achieve conditional anonymity for device groups. Specifically, a flexible and secure binding method is first developed by leveraging Chinese Remainder Theorem (CRT) to bind user with device groups. Each device’s CRT modulus is derived from the key sequence to construct many-to-many user-device binding relationships, which are managed in the form of on-chain Pedersen Commitment. Moreover, we design an identity anonymizing and tracing method for device groups. The key sequence is regarded as traceable device pseudo-identities, and then inserted into the cuckoo filter to reduce the on-chain storage overhead and mitigate malicious login attempts with low costs. Based on above two methods, the combination of Pedersen Commitment and Zero-Knowledge Proof of Knowledge is used to achieve user-device authentication with conditional anonymity. The security analysis was presented to demonstrate important security properties. A proof-of-concept prototype was implemented to conduct performance evaluation and comparative analysis.