Paper 2019/718

Improved Building Blocks for Secure Multi-Party Computation based on Secret Sharing with Honest Majority

Marina Blanton, Ahreum Kang, and Chen Yuan

Abstract

Secure multi-party computation permits evaluation of any desired functionality on private data without disclosing the data to the participants and is gaining its popularity due to increasing collection of user, customer, or patient data and the need to analyze data sets distributed across different organizations without disclosing them. Because adoption of secure computation techniques depends on their performance in practice, it is important to continue improving their performance. In this work, we focus on common non-trivial operations used by many types of programs, and any advances in their performance would impact the runtime of programs that rely on them. In particular, we treat the operation of reading or writing an element of an array at a private location and integer multiplication. The focus of this work is secret sharing setting with honest majority in the semi-honest security model. We demonstrate improvement of the proposed techniques over prior constructions via analytical and empirical evaluation.