Efficient Multi-party Computation Algorithm Design for Real-World Applications

Zengxiang Li, Chutima Kitcharoenpaisan, Phond Phunchongharn, Yechao Yang, Rick Siow Mong Goh, Yusen Li

Abstract: Secure Multi-Party Computation (MPC) is a promising privacy-preserving technology to enable multiple trustless parties to compute a function jointly without revealing private inputs to each other. With the fast development of MPC protocols, software implementation, and underlying computation infrastructure, MPC has developed from purely theoretical interest to tangible platform implementations for real-world applications. In this paper, we investigate multiple mechanisms to design efficient MPC algorithms by avoiding costly MPC operations and leveraging parallel operations. In order to speed up database table searching, a machine learning-based approach is proposed to completely avoid equality-check operations, playing the trade-off between efficiency and accuracy. According to our experimental results, a well-designed MPC algorithm could improve performance and scalability significantly, and thus make MPC technology practicable.