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 right decision. There are numerous mature technologies for mining self-owned data of a party. However, how to perform distributed data mining to obtain information from data owned by multi-party without privacy leak is still a challenge. In recent years, secure multi-party computation (MPC) shows its potential capability for solving this problem. Meanwhile, there are a number of problems to be solved before utilizing in business environment. In this paper, we point out two unsupported tasks of MPC that are common in real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these in MPC context requirements. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results demonstrate that our design and implementation are feasible and effective for privacy preserving distributed data mining.