Efficient Secure Multi-party Computation for Multi-dimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made practical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC protocols more efficient. We will discuss the generation process, the usage, as well as the applications of tensor triples and show that it can accelerate privacy-preserving biometric identification protocols, such as FingerCode, Eigenfaces and FaceNet, by more than 1000 times, with reasonable offline costs.