Fuzzy Commitment Scheme Research Articles

Statistical attack against fuzzy commitment scheme

In this study a statistical attack against fuzzy commitment schemes is presented. Comparisons of different pairs of binary biometric feature vectors yield binomial distributions, the standard deviations of which are bounded by the entropy of biometric templates. In case error correction consists of a series of chunks, like in the vast majority of approaches, helper data become vulnerable to statistical attacks. Error-correction codewords are bound to separate parts of a binary template among which biometric entropy is dispersed. As a consequence, chunks of the helper data are prone to statistical significant false acceptance. In experimental evaluations the proposed attack is applied to different iris-biometric fuzzy commitment schemes retrieving cryptographic keys at alarming low effort.

An effective biometric cryptosystem combining fingerprints with error correction codes

With the emergence and popularity of identity verification means by biometrics, the biometric system which can assure security and privacy has received more and more concentration from both the research and industry communities. In the field of secure biometric authentication, one branch is to combine the biometrics and cryptography. Among all the solutions in this branch, fuzzy commitment scheme is a pioneer and effective security primitive. In this paper, we propose a novel binary length-fixed feature generation method of fingerprint. The alignment procedure, which is thought as a difficult task in the encrypted domain, is avoided in the proposed method due to the employment of minutiae triplets. Using the generated binary feature as input and based on fuzzy commitment scheme, we construct the biometric cryptosystems by combining various of error correction codes, including BCH code, a concatenated code of BCH code and Reed–Solomon code, and LDPC code. Experiments conducted on three fingerprint databases, including one in-house and two public domain, demonstrate that the proposed binary feature generation method is effective and promising, and the biometric cryptosystem constructed by the feature outperforms most of the existing biometric cryptosystems in terms of ZeroFAR and security strength. For instance, in the whole FVC2002 DB2, a 4.58% ZeroFAR is achieved by the proposed biometric cryptosystem with the security strength 48 bits.

Preventing the Decodability Attack Based Cross-Matching in a Fuzzy Commitment Scheme

Template protection techniques are used within biometric systems in order to safeguard the privacy of the system's subjects. This protection also includes unlinkability, i.e., preventing cross-matching between two or more reference templates from the same subject across different applications. In the literature, the template protection techniques based on fuzzy commitment, also known as the code-offset construction, have recently been investigated. Recent work presented the decodability attack vulnerability facilitating cross-matching based on the protected templates and its theoretical analysis. First, we extend the theoretical analysis and include the comparison between the system and cross-matching performance. We validate the presented analysis using real biometric data from the MCYT fingerprint database. Second, we show that applying a random bit-permutation process secures the fuzzy commitment scheme from cross-matching based on the decodability attack.

Information Leakage in Fuzzy Commitment Schemes

In 1999, Juels and Wattenberg introduced the fuzzy commitment scheme. This scheme is a particular realization of a binary biometric secrecy system with chosen secret keys. It became a popular technique for designing biometric secrecy systems, since it is convenient and easy to implement using standard error-correcting codes. This paper investigates privacy- and secrecy-leakage in fuzzy commitment schemes. The analysis is carried out for four cases of biometric data statistics, i.e., memoryless totally symmetric, memoryless input-symmetric, memoryless, and stationary ergodic. First, the achievable regions are determined for the cases when data statistics are memoryless totally symmetric and memoryless input-symmetric. For the general memoryless and stationary ergodic cases, only outer bounds for the achievable rate-leakage regions are provided. These bounds, however, are sharpened for systematic parity-check codes. Given the achievable regions (bounds), the optimality of fuzzy commitment is assessed. The analysis shows that fuzzy commitment is only optimal for the memoryless totally symmetric case if the scheme operates at the maximum secret-key rate. Moreover, it is demonstrated that for the general memoryless and stationary ergodic cases, the scheme leaks information on both the secret and biometric data.

Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In recent years, the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper-data system, fuzzy extractors, fuzzy vault, and cancelable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECCs) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance (HD) or bit errors between the binary vectors obtained from the enrollment and verification phases, respectively. If the HD is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate tradeoff. A method to improve the FRR consists of using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the HD. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this paper, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The error-detection tradeoff curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the Face Recognition Grand Challenge v2 and Fingerprint Verification Competition 2000 biometric databases. </para>

A Hybrid Approach for Generating Secure and Discriminating Face Template

Biometric template protection is one of the most important issues in deploying a practical biometric system. To tackle this problem, many algorithms, that do not store the template in its original form, have been reported in recent years. They can be categorized into two approaches, namely biometric cryptosystem and transform-based. However, most (if not all) algorithms in both approaches offer a trade-off between the template security and matching performance. Moreover, we believe that no single template protection method is capable of satisfying the security and performance simultaneously. In this paper, we propose a hybrid approach which takes advantage of both the biometric cryptosystem approach and the transform-based approach. A three-step hybrid algorithm is designed and developed based on random projection, discriminability-preserving (DP) transform, and fuzzy commitment scheme. The proposed algorithm not only provides good security, but also enhances the performance through the DP transform. Three publicly available face databases, namely FERET, CMU-PIE, and FRGC, are used for evaluation. The security strength of the binary templates generated from FERET, CMU-PIE, and FRGC databases are 206.3, 203.5, and 347.3 bits, respectively. Moreover, noninvertibility analysis and discussion on data leakage of the proposed hybrid algorithm are also reported. Experimental results show that, using Fisherface to construct the input facial feature vector (face template), the proposed hybrid method can improve the recognition accuracy by 4%, 11%, and 15% on the FERET, CMU-PIE, and FRGC databases, respectively. A comparison with the recently developed random multispace quantization biohashing algorithm is also reported.

Theoretical and Practical Boundaries of Binary Secure Sketches

Fuzzy commitment schemes, introduced as a link between biometrics and cryptography, are a way to handle biometric data matching as an error-correction issue. We focus here on finding the best error-correcting code with respect to a given database of biometric data. We propose a method that models discrepancies between biometric measurements as an erasure and error channel, and we estimate its capacity. We then show that two-dimensional iterative min-sum decoding of properly chosen product codes almost reaches the capacity of this channel. This leads to practical fuzzy commitment schemes that are close to theoretical limits. We test our techniques on public iris and fingerprint databases and validate our findings.

Secure biometric template protection in fuzzy commitment scheme

Fuzzy Commitment Scheme is a popular technique for biometric template protection. The idea is to bind an random bit string with a biometric template in binary format called difference vector. Ideally, a difference vector is infeasible to recover either the biometric template or the random bit string without any knowledge of the user's biometric data. Yet, this is only valid if the biometric template is uniformly random, which is not the case in reality. In this paper, we propose a method known as Randomized Dynamic Quantization transformation to binarize biometric data. The transformed bit strings are highly distinctive among the users and near to uniformly random. We demonstrate the implementation in the context of fingerprint biometrics.