TriFSS: Secure Trigonometric Function Evaluation via Function Secret Sharing

Pengzhi Xing, Hongwei Li, Meng Hao, Hanxiao Chen, Shengke Zeng

Abstract: Trigonometric functions are crucial non-linear operations used in scientific computation and complex machine learning models. However, existing secure computing frameworks either lack support for these operations, or suffer from undesirable performance bottleneck. In this paper, we present an efficient and precise fixed-point framework called TriFSS for securely evaluating trigonometric functions. Specifically, we first design new building blocks based on advanced Function Secret Sharing techniques, achieving reduced communication and computation overhead. Second, with these efficient components, we propose a general evaluation process for these functions, in which periodic properties are fully exploited for better performance. Moreover, we implement the TriFSS framework and conduct extensive experiments. The experimental results show that our protocols achieve at least 23x less communication overhead and 2.8x less latency than the state-of-the-art frameworks, while only resulting in 1 ULP error, which is comparable to floating-point based works.