Welcome to the resource topic for 2025/1189
Title:
Performance and Privacy: A Low-Latency Secure Anonymous Authentication Protocol with OPRF
Authors: Wenjv Hu, Yanping Ye, Yin Li
Abstract:erforming privacy-preserving queries, particularly anonymous authentication, against large-scale datasets presents critical tradeoffs between security, latency, scalability. Existing cryptographic solutions often impose linear
computation or communication overheads. This paper introduces a novel,
efficient protocol for secure anonymous authentication, uniquely combining matrix partitioning via hash prefixes with Oblivious Pseudorandom Functions in a
three-server semi-honest model. Crucially, compared to our previous work published at TrustCom 2024, this enhanced protocol eliminates the dependency on a
designated fully trusted server, achieving security when any single server is corrupted. Furthermore, our protocol demonstrates significant performance improvements over current state-of-the-art methods. It achieves sub-linear online
communication complexity. Evaluations show that for datasets of size 𝑚 ≈ 106
,
our protocol reduces online communication by at least 30% compared to other
sub-linear schemes, while maintaining competitive online computation times. Security is proven via simulation, and comprehensive experiments confirm practicality for datasets up to 𝑚 = 10^8
ePrint: https://eprint.iacr.org/2025/1189
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