Shortcut: Making MPC-based Collaborative Analytics Efficient on Dynamic Databases
Peizhao Zhou, Xiaojie Guo, Pinzhi Chen, Tong Li, Siyi Lv, Zheli Liu
2024年份
5被引次数
2顶会引用
摘要
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.
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