Privacy-Preserving Computation:A Comprehensive Survey of Methods and Applications

Abstract

This paper presents a comprehensive review of privacy-preserving computation, including its various methods, such as Trusted Environment Execution (TEE) computation, Secure Multi-Party Computation (SMPC) , Federated Learning (FL) , Differential Privacy (DP) , and Private Information Retrieval (PIR) , et. It also analyzes and compares these methods from the aspects of security, advantages/disadvantages, and risks. Additionally, this paper investigates the applications and development of privacy-preserving computation, which finally demonstrates that privacy-preserving computation has a significant contribution on data circulation and data value realization. At last, the paper analyzes the current situation and challenges of privacy- preserving computation, while pointing out the future direction of it.