Privacy-Preserving Similarity Evaluation and Application to Remote Biometrics Authentication

Authors:
Hiroaki Kikuchi;Kei Nagai;Wakaha Ogata;Masakatsu Nishigaki
Affiliations:
Department of Communication and Network Engineering, School of Information and Telecommunication Engineering, Tokai university, Kangawa, Japan 259-1292;Department of Communication and Network Engineering, School of Information and Telecommunication Engineering, Tokai university, Kangawa, Japan 259-1292;Graduate School of Innovation Management, Tokyo Institute of Technology, Tokyo, Japan;Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan
Venue:
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2008

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Abstract

In this paper, a new method for secure remote biometric authentication preventing the vulnerability of compromised biometrics is presented. The idea is based on a public-key cryptographical protocol, referred as Zero-knowledge Proof, which allows a user to prove that she has surely a valid biometric data without revealing the data. Hence, the scheme is free from the risk of disclosure of biometric data. Even if a malicious administrator has a privilege access to the private database, it is infeasible for him to learn the private template. This paper studies two well-known definitions, the cosine correlationand the Euclidean distanceas similarities of given two feature vectors. Both similarities are defined with some multiplications and additions, which can be performed in privacy-preserving way because of the useful property of public-key commitment scheme, additive homomorphism. The estimation based on the experimental implementation shows that the private Euclidean distance scheme archives better accuracy in terms of false acceptance and rejection than the private cosine coloration scheme, but it requires about 5/2 n茂戮驴 overhead to evaluate n-dimension feature vectors consisting of 茂戮驴-bit integers.