Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC
This work addresses the practical deployment challenges of privacy-preserving deep learning for applications requiring secure data collaboration, though it is incremental in nature.
The paper tackles the high overhead of secure multi-party computation (MPC) in privacy-preserving deep learning by proposing a low-latency design that reduces communication rounds and optimizes nonlinear function computations, achieving a 10-20% speedup in communication latency.
Secure multi-party computation (MPC) facilitates privacy-preserving computation between multiple parties without leaking private information. While most secure deep learning techniques utilize MPC operations to achieve feasible privacy-preserving machine learning on downstream tasks, the overhead of the computation and communication still hampers their practical application. This work proposes a low-latency secret-sharing-based MPC design that reduces unnecessary communication rounds during the execution of MPC protocols. We also present a method for improving the computation of commonly used nonlinear functions in deep learning by integrating multivariate multiplication and coalescing different packets into one to maximize network utilization. Our experimental results indicate that our method is effective in a variety of settings, with a speedup in communication latency of $10\sim20\%$.