Anonymous credentials are an essential cryptography primitive to protect user privacy and provide fine-grained access control for proving ownership and rights of specific credentials. There are currently two roadmaps to designing anonymous credentials: one is signature credentials, which are constructed by signature with efficient protocols and non-interactive zero-knowledge proofs, and the other is functional credentials, which are transformed from predicate encryption schemes. However, none of the existing instances of anonymous credentials support$expressive$access policies expressed as conjunction, disjunction, or arbitrary Boolean formulas, which are particularly useful for cloud services. In this paper, we propose a new fast and expressive anonymous credential, called FEAC. It is constructed with the unique$dual$$randomness$$splitting$technique, which combines the most efficient anonymous key-policy attribute-based encryption (USENIX 24) and short randomizable signature (CT-RSA 18) to balance efficiency, expressiveness, and security, demonstrating a new way to instantiate anonymous credentials. Furthermore, our credential presentation protocol offloads most of the time-consuming computation to the cloud server (11 pairing) to reduce the computational burden on the user side (2 pairing). We propose formal definitions and formal security proofs of FEAC. We provide implementations and evaluate the performance of FEAC, comparing it to state-of-the-art work.

Underwater acoustic sensor network (UASN) is a highly practical and popular sensor network extensively utilized for marine environmental monitoring and underwater exploration. Due to the high frequency of node usage, limited energy resources and high QoS performance requirements in UASN, it is necessary to adopt a comprehensive and integrated routing algorithm. In this paper, an innovative comprehensive clustering routing model is designed to accurately reflect the practical usage of UASN. Furthermore, a novel clustering routing algorithm, namely multi-objective differential chaotic shuffled frog leaping algorithm (MDCSFLA), is proposed. This algorithm effectively extends the network lifetime, greatly optimizes network energy balance, and improves network QoS performance. Additionally, by designing a novel differential local search strategy and chaotic perturbation strategy, the global optimization capability is significantly enhanced, and the convergence speed of the algorithm is effectively improved. Through a series of experiments, it is demonstrated that compared to LEACH-C, Q-LEACH, OptGACHE, and UCPSO, the proposed approach demonstrates a significant improvement in network lifetime and throughput, with an increase of at least 22.70% and 27.51%, respectively. Additionally, it achieves substantial reductions of at least 5.92% in average data transmission latency and 16.04% in packet loss rate.

The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy“no read” and no write” security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.