Privacy Preserving Key Generation for Iris Biometrics

Rathgeb, Christian; Uhl, Andreas

doi:10.1007/978-3-642-13241-4_18

Cited by 30 publications

(8 citation statements)

References 16 publications

(34 reference statements)

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“…The best Genuine Acceptance Rate (GAR) reported was 83.4% and 91.1% respectively for CASIAv1 and CASIAv3 iris databases (Center for Biometrics and Security Research, Beijing, China) under adequate system security. Rathgeb et al [28] have proposed an iris key generation scheme based on interval mapping for iris features in real values. The highest key generation rate reported on CASIAv3 was 95.09% for five enrollment samples.…”

Section: Related Work Problem Definitionmentioning

confidence: 99%

Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

2021

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Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.

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Section: Related Work Problem Definitionmentioning

confidence: 99%

Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

2021

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“…Privacy-preserving finger code authentication [25], and privacy-preserving key generation for iris biometrics [26] are two approaches that apply cryptographic methods to maintain the privacy of fingerprint and iris data. However, these solutions also need more efficient procedures, as cryptographic approaches are inefficient in calculations [26,27]. Privacy-preserving fingerprint and iris analysis can be possible future applications for PEEP, but this needs further investigation.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Face Recognition Utilizing Differential Privacy

Chamikara

Bertók

Khalil

et al. 2020

Computers & Security

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The published article can be found at https://doi.org/10. 1016/j.cose.2020.101951 Facial recognition technologies are implemented in many areas, including but not limited to, citizen surveillance, crime control, activity monitoring, and facial expression evaluation. However, processing biometric information is a resource-intensive task that often involves third-party servers, which can be accessed by adversaries with malicious intent. Biometric information delivered to untrusted thirdparty servers in an uncontrolled manner can be considered a significant privacy leak (i.e. uncontrolled information release) as biometrics can be correlated with sensitive data such as healthcare or financial records. In this paper, we propose a privacy-preserving technique for "controlled information release", where we disguise an original face image and prevent leakage of the biometric features while identifying a person. We introduce a new privacy-preserving face recognition protocol named PEEP (Privacy using EigEnface Perturbation) that utilizes local differential privacy. PEEP applies perturbation to Eigenfaces utilizing differential privacy and stores only the perturbed data in the third-party servers to run a standard Eigenface recognition algorithm. As a result, the trained model will not be vulnerable to privacy attacks such as membership inference and model memorization attacks. Our experiments show that PEEP exhibits a classification accuracy of around 70% -90% under standard privacy settings. individual face recognition for unlocking a mobile device to crowd surveillance. Companies have also invested heavily in this field; Google's facial recognition in the Google Glass project [1], Facebook's DeepFace technology [2], and Apple's patented face identification system [3] are examples of the growing number of facial identification systems. Existing face recognition technologies and the widespread use of biometrics introduce a serious threat to individuals' privacy, exacerbated by the fact that biometric identification is often done quietly, without proper consent from observed people. For example, the UK uses an estimated 4.2 million surveillance cameras to monitor public areas [4]. However, it is not feasible to obtain explicit consent from an extremely large number of persons being watched.Nevertheless, facial images directly reflect the owners' identity, and they can be easily linked to other sensitive information such as health records and financial records, raising privacy concerns. Biometric data analysis systems often need to employ high-performance third-party servers to conduct complex computational operations on large numbers of biometric data inputs. However, these third-party servers can be accessed by untrusted parties causing privacy issues.Among different definitions, information privacy can be defined as the "controlled information release" that permits an anticipated level of utility via a private function that protects the identity of the data owners [5]. Privacy-preserving face recognition involves at l...

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“…Rathgeb and Uhl [86] extended the scheme of [82] to iris biometrics generating 128-bit keys. In [87] the authors apply a context-based reliable component selection and construct intervals for the most reliable features of each subject.…”

Section: ) Quantization Schemesmentioning

confidence: 99%

A survey on biometric cryptosystems and cancelable biometrics

Rathgeb

Uhl

2011

EURASIP J. on Info. Security

Self Cite

496

337

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Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

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Privacy Preserving Key Generation for Iris Biometrics

Cited by 30 publications

References 16 publications

Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Privacy Preserving Face Recognition Utilizing Differential Privacy

A survey on biometric cryptosystems and cancelable biometrics

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