Efficient Privacy-Preserving Biometric Identification

We are developing protocols and implementations that enable two parties to privately perform biometric identification.


Yan Huang, University of Virginia
Lior Malka, University of Maryland (now at Intel)
David Evans, University of Virginia
Jonathan Katz, University of Maryland)


Yan Huang, Lior Malka, David Evans, and Jonathan Katz. Efficient Privacy-Preserving Biometric Identification, in 18th Network and Distributed System Security Symposium (NDSS 2011), 6-9 February 2011. [PDF, 14 pages]

We present an efficient matching protocol that can be used in many privacy-preserving biometric identification systems in the semi-honest setting. Our most general technical contribution is a new backtracking protocol that uses the by-product of evaluating a garbled circuit to enable efficient oblivious information retrieval. We also present a more efficient protocol for computing the Euclidean distances of vectors, and optimized circuits for finding the closest match between a point held by one party and a set of points held by another. We evaluate our protocols by implementing a practical privacy-preserving fingerprint matching system.


Yan Huang's talk at NDSS 2011, 9 February 2011. [PDF (2.6MB)]

Poster for University of Virginia Engineering Research Symposium: [PDF (13MB)]


Secure Biometrics (Version 0.1)

secure-biometrics-0.1.tgz [README]

Library and framework for efficient, privacy-preserving biometric identification. This software package is made freely available under the MIT license.