CCS2024

Shortcut: Making MPC-based Collaborative Analytics Efficient on Dynamic Databases

Peizhao Zhou, Xiaojie Guo, Pinzhi Chen, Tong Li, Siyi Lv, Zheli Liu

5 citations

Abstract

Secure Multi-party Computation (MPC) provides a promising solution for privacy-preserving multi-source data analytics. However, existing MPC-based collaborative analytics systems (MCASs) have unsatisfying performance for scenarios with dynamic databases. Naively running an MCAS on a dynamic database would lead to significant redundant costs and raise performance concerns, due to the substantial duplicate contents between the pre-updating and post-updating databases.