With the standardization of post-quantum signature schemes such as Dilithium and Falcon, lattice-based ring signatures have become a promising tool for anonymous authentication and privacy-preserving encryption in big data environments. Specifically, the DualRing-LB scheme (CRYPTO’ 21), despite its short signatures and elegant structure, suffers from the large public key size, which dominates its communication cost and hinders scalability. There are two methods to reduce the public key size: the first is a compression technique, and the second is the bimodal distribution. The former discards the lower bits of the public key while the latter utilizes a smaller modulus. However, the unforgeability proof of DualRing-LB is incompatible with these methods because they lead to a rapid increase in the norm bound of the module short integer solution (or MSIS) problem, thereby significantly reducing security. In this paper, we follow the original security method of DualRing-LB and apply the trapdoor sampling technique to derive a tighter upper bound of MSIS problem, yielding a fixed security level independent of the ring size. The new proof result enables the use of both the compression technique and the bimodal distribution to enhance the communication efficiency of lattice-based DualRing schemes. Based on this result, we propose three practical lattice-based instantiations that reduce the public key size by 56%, 67%, 50%, compared to DualRing-LB, respectively, and achieve overall size reductions of at least 52%, 56%, 38%, respectively, across different ring sizes. Our results can contribute to lightweight, privacy-preserving cryptographic mechanisms for secure data sharing and authentication in large-scale distributed systems.
Guangyu Liao, Zengpeng Li, Guangsheng Feng et al.· International Journal of Inf...· 0 citations
Semantic search systems map data to a high-dimensional vector space and support retrieval of similar data via approximate nearest neighbor search. When the system is hosted by an untrusted cloud provider, there is no privacy for the data or the query. Our goal is to design a system with three properties: privacy, accuracy, and efficiency. Existing works adopt either homomorphic encryption (HE), oblivious RAM (ORAM), or a differential privacy (DP) approach. They fall short of achieving all three properties. In this paper, we present MESS, a system that realizes our goal. It maps the original vectors into binary codes, applies locality-sensitive hashing (LSH) and randomized response, and constructs a multi-graph Hierarchical Navigable Small World (HNSW) index over the perturbed codes. MESS ensures data, query, and access pattern privacy. It also ensures search pattern privacy via a two-phase query perturbation mechanism. The multi-graph index mitigates the impact of perturbation on result quality, thereby achieving accuracy. MESS is efficient because search is performed directly over perturbed codes, without the overhead of homomorphic encryption or ORAM. We give formal analysis of the system's privacy and extensive evaluation of its performance. The results show that MESS achieves up to 15.08\times lower latency than state-of-the-art baselines.
Haoyu Cui, Zengpeng Li, Tien Tuan Anh Dinh et al.· 1 citation· ⚡1
Secure Federated Learning protects user privacy by keeping local gradients within the ciphertext space, thereby preventing direct exposure of raw gradients during aggregation. Paradoxically, this very protection introduces a fundamental vulnerability: the opacity of encrypted gradients blinds the server, rendering honest and malicious gradients indistinguishable. An attacker can exploit this opacity to inject manipulated gradients directly into the secure aggregation process, compromising the integrity of the global model. To address this issue, we propose SFLBR, a novel Byzantine-robust Secure Federated Learning framework. Specifically, we adopt the gradient median as a robust benchmark and introduce a layer-wise cosine similarity metric to differentiate malicious gradients. Furthermore, considering the complexity of non-independent and identically distributed (Non-IID) scenarios, we further design a proactive defense strategy that constrains the divergence among honest gradients, and augment it with a trust score function to improve robustness against potential misjudgments. Finally, we construct a series of secure communication protocols based on a lightweight dual-masking encryption mechanism, which enable efficient robust aggregation directly within the ciphertext space. Extensive experiments validate the effectiveness of SFLBR, demonstrating state-of-the-art (SOTA) defense performance across diverse attack scenarios.
Jinbo Wang, Ruijin Wang, Zengpeng Li et al.· IEEE Transactions on Informa...· 0 citations
