Practical Biometric Authentication with Template Protection

Tuyls, Pim; Akkermans, A.H.M.; Kevenaar, Tom A. M.; Schrijen, Geert-Jan; Bazen, A.M.; Veldhuis, R.N.J.

doi:10.1007/11527923_45

Cited by 233 publications

(246 citation statements)

References 14 publications

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“…To solve this problem, previous methods generated a stable key from facial features of each person. Of these previous methods, we focus on the methods that quantize the facial features to generate a stable key [2] [3].…”

Section: Problem Associated With Previously Developed Methodsmentioning

confidence: 99%

Privacy-Aware Database System for Retrieving Facial Images

Fujita

Funatomi

Morimura

et al. 2012

Communications in Computer and Information Science

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Abstract. To achieve privacy protection on facial image retrieval systems, we propose a method of encrypting facial images with a key produced from facial features. Because facial features vary even for the same person, it is not recommended to use facial features as the cryptographic key. Therefore, we propose a method for generating a key by quantizing the facial features based on entropy. In our experiment, we applied the proposed method to a public facial image database, and evaluated the system performance and integrity by calculating the false acceptance rate and the false rejection rate.

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Section: Problem Associated With Previously Developed Methodsmentioning

confidence: 99%

Privacy-Aware Database System for Retrieving Facial Images

Fujita

Funatomi

Morimura

et al. 2012

Communications in Computer and Information Science

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“…To describe the shape of the fingerprint, two types of features are extracted. The first feature vector is the squared directional field and the second feature vector is the Gabor response of the fingerprint, details can be found in Tuyls et al [13]. The resulting feature vector is a concatenation of the squared directional field and the Gabor response and describes the global shape of the fingerprint in 1536 elements.…”

Section: Lemma 2 ( -Indistinguishability For Qim-fuzzy Embedder)mentioning

confidence: 99%

A Quantitative Analysis of Indistinguishability for a Continuous Domain Biometric Cryptosystem

Buhan

Breebaart

Guajardo

et al. 2010

Lecture Notes in Computer Science

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Abstract. Biometric information is regarded as highly sensitive information and therefore encryption techniques for biometric information are needed to address security and privacy requirements of biometric information. Most security analyses for these encryption techniques focus on the scenario of one user enrolled in a single biometric system. In practice, biometric systems are deployed at different places and the scenario of one user enrolled in many biometric systems is closer to reality. In this scenario, cross-matching (tracking users enrolled in multiple databases) becomes an important privacy threat. To prevent such cross-matching, various methods to create renewable and indistinguishable biometric references have been published. In this paper, we investigate the indistinguishability or the protection against cross-matching of a continuous-domain biometric cryptosystem, the QIM. In particular our contributions are as follows. Firstly, we present a technique, which allows an adversary to decide whether two protected biometric reference data come from the same person or not. Secondly, we quantify the probability of success of an adversary who plays the indistinguishability game and thirdly, we compare the probability of success of an adversary to the authentication performance of the biometric system for the MCYT fingerprint database. The results indicate that although biometric cryptosystems represent a step in the direction of privacy enhancement, we are not there yet.

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“…Another distance measure, point-set difference, motivated from a popular representation for fingerprint features, is investigated in a number of studies [5,3,4]. A different approach [14,24,23] focuses on information leakage defined using Shannon entropy on continuous data with known distributions.…”

Section: Related Workmentioning

confidence: 99%

Secure Sketch for Multiple Secrets

Fang

Chang

2010

Applied Cryptography and Network Security

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Abstract. Secure sketches are useful in extending cryptographic schemes to biometric data since they allow recovery of fuzzy secrets under inevitable noise. In practice, secrets derived from biometric data are seldom used alone, but typically employed in a multi-factor or a multimodality setting where multiple secrets with different roles and limitations are used together. To handle multiple secrets, we can generate a sketch for each secret independently and simply concatenate them. Alternatively, we can "mix" the secrets and individual sketches, for example, by taking the first secret as the key to encrypt the sketches of all other secrets. Hence, it is interesting to investigate how the secrets are to be mixed so as to cater for different requirements of individual secrets. We found that, by appropriate mixing, entropy loss on more important secrets (e.g., biometrics) can be "diverted" to less important ones (e.g., password or PIN), thus providing more protection to the former. On the other hand, we found that mixing may not be advisable if the amount of randomness invested in sketch construction is large, or the sketch contains high redundancy, or all secrets are of the same importance. Our analysis provides useful insights and guidelines in the applications of secure sketches in biometric systems.

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Practical Biometric Authentication with Template Protection

Cited by 233 publications

References 14 publications

Privacy-Aware Database System for Retrieving Facial Images

Privacy-Aware Database System for Retrieving Facial Images

A Quantitative Analysis of Indistinguishability for a Continuous Domain Biometric Cryptosystem

Secure Sketch for Multiple Secrets

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