Secure computation of hidden Markov models

Abstract

Hidden Markov Model (HMM) is a popular statistical tool with a large number of applications in pattern recognition. In some of such applications, including speaker recognition in particular, the computation involves personal data that can identify individuals and must be protected. For that reason, we develop privacy-preserving techniques for HMM and Gaussian mixture model (GMM) computation suitable for use in speaker recognition and other applications. Unlike prior work, our solution uses floating point arithmetic, which allows us to simultaneously achieve high accuracy, provable security guarantees, and reasonable performance. We develop techniques for both two-party HMM and GMM computation based on threshold homomorphic encryption and multi-party computation based on threshold linear secret sharing, which are suitable for secure collaborative computation as well as secure outsourcing.