Secure Computation of Hidden Markov Models

Mehrdad Aliasgari, Marina Blanton

2013

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.

Paper Citation

in Harvard Style

Aliasgari M. and Blanton M. (2013). Secure Computation of Hidden Markov Models . In Proceedings of the 10th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2013) ISBN 978-989-8565-73-0, pages 242-253. DOI: 10.5220/0004533502420253

in Bibtex Style

@conference{secrypt13,
author={Mehrdad Aliasgari and Marina Blanton},
title={Secure Computation of Hidden Markov Models},
booktitle={Proceedings of the 10th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2013)},
year={2013},
pages={242-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004533502420253},
isbn={978-989-8565-73-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2013)
TI - Secure Computation of Hidden Markov Models
SN - 978-989-8565-73-0
AU - Aliasgari M.
AU - Blanton M.
PY - 2013
SP - 242
EP - 253
DO - 10.5220/0004533502420253