Secure Sketch for Biometric Templates

Abstract

There have been active discussions on how to derive a consistent cryptographic key from noisy data such as biometric templates, with the help of some extra information called a sketch. It is desirable that the sketch reveals little information about the biometric templates even in the worst case (i.e., the entropy loss should be low). The main difficulty is that many biometric templates are represented as points in continuous domains with unknown distributions, whereas known results either work only in discrete domains, or lack rigorous analysis on the entropy loss. A general approach to handle points in continuous domains is to quantize (discretize) the points and apply a known sketch scheme in the discrete domain. However, it can be difficult to analyze the entropy loss due to quantization and to find the "optimal" quantizer. In this paper, instead of trying to solve these problems directly, we propose to examine the relative entropy loss of any given scheme, which bounds the number of additional bits we could have extracted if we used the optimal parameters. We give a general scheme and show that the relative entropy loss due to suboptimal discretization is at most (n log 3), where n is the number of points, and the bound is tight. We further illustrate how our scheme can be applied to real biometric data by giving a concrete scheme for face biometrics.

Generic Constructions of Biometric Identity Based Encryption Systems

“…In order to provide anonymity, we assume that biometrics consists of an ordered set of features as in face biometrics [15]. An interesting future work can be the design of generic constructions of anonymous biometric IBE schemes, where biometrics can be represented as an unordered set of features, which is the case for some biometric modalities.…”

“…N, u, H), where H is a hash function H : BID → J(N ), where BID = W × ID. We assume that the features w j ∈ W are ordered as in [15]. The master key is msk = (p, q, K), namely the factorization of N together with a random key K for a pseudorandom function F K : W × ID → {0, 1, 2, 3}.…”

A Quantitative Analysis of Indistinguishability for a Continuous Domain Biometric Cryptosystem

“…Li, et. al [8] argue that performance measures like min-entropy or entropy-loss are the result of the quantization parameters used. The larger the quantization step, the less entropy is left in the discrete biometric data and the easier it is to reconstruct the secret k vice-versa the smaller the quantization step, the more entropy remains in the discrete biometric and the harder it is to reconstruct k.…”

Security enhancement of biometrics, cryptography and data hiding by their combinations

“…It could help to handle biometric data matching as an error correction issue. The scheme with quantization is applied to face biometrics and good results are obtained in [17]with the average key size of 73 bits.…”