ACCO: Algebraic Computation with Comparison

Abstract

We propose ACCO: the first maliciously secure multiparty computation engine in the honest majority setting, which also supports secure and efficient comparison and integer truncation. Our system is also the first to achieve information theoretic security. We use ACCO to build an information theoretic privacy preserving machine learning system where a set of parties collaboratively train regression models in the presence of a malicious adversary. We report an implementation of our system and compare the performance against Helen, the work of Zheng, Popa, Gonzalez and Stoica (SP'19) which provided multiparty regression models secure against malicious adversaries. Our system offers a significant speedup over Helen.