Efficient Multiparty Computation Algorithm Design for RealWorld Applications Secure MultiParty Computation (MPC) is a promising privacypreserving 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 realworld 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 learningbased approach is proposed to completely avoid equalitycheck operations, playing the tradeoff between efficiency and accuracy. According to our experimental results, a welldesigned MPC algorithm could improve performance and scalability significantly, and thus make MPC technology practicable.

Privacy preserving distributed data mining based on secure multiparty computation Abstract   Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining selfowned 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 multiparty 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 realworld. Towards this end, we design algorithms based on optimized matrix computation with onehot 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.