Efficient and secure multi-party computation protocol supporting deep learning

Abstract

Privacy-preserving deep learning based on secure multi-party computation (MPC) has emerged as a critical research focus in recent years. While existing approaches predominantly employ additive secret sharing with a fixed number of parties, they have yet to fully leverage the more efficient Shamir-based schemes. However, the adoption of Shamir secret sharing faces two key challenges: limitations of decimal computation and signed number representation. Furthermore, current solutions often lack optimization for specific computational modules and rely on conventional methods ill-suited for MPC environments. To address these issues, this paper proposes a fixed-point decimal-supported Shamir secret sharing scheme. A key innovation is our truncation algorithm, which effectively manages the expanded decimal digits resulting from multiplication operations, enabling comprehensive fixed-point arithmetic within the Shamir-based MPC framework. Extensive large-scale simulations validate the accuracy of our truncation method. Moreover, we introduce optimized protocols for two crucial deep learning operations: convolution and Softmax function computation. Our convolution protocol leverages the Winograd algorithm to significantly reduce multiplication gate count, yielding over 50% performance improvement. For Softmax computation, we extend existing two-party protocols to a multi-party Shamir setting, developing the nQSMax algorithm. This algorithm achieves exceptional accuracy exceeding 99% within seconds, requiring only a few iterations.