Privacy preserving distributed data mining based on secure multi-party computation

Abstract

Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining self-owned data of a party are rather mature. However, how to perform distributed data mining to obtain information from data owned by multiple parties without privacy leakage remains a big challenge. While secure multi-party computation (MPC) may potentially address this challenge, several issues have to be overcome for practical realizations. In this paper, we point out two unsupported tasks of MPC that are common in the real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results show that our design and implementation are feasible and effective for privacy preserving distributed data mining.