Privacy-Preserving Indexing of Iris-Codes with Cancelable Bloom Filter-based Search Structures

Drozdowski, Pawel; Garg, Surabhi; Rathgeb, Christian; Gomez-Barrcro, M.; Chang, Donghoon; Busch, Christoph

doi:10.23919/eusipco.2018.8553053

Cited by 19 publications

(8 citation statements)

References 23 publications

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“…A method for archiving protected iris templates, which essentially seeks to minimize tasks in computationally intensive verification mode, is proposed [24]. It also focused on using arbitrary permutations in iris code rows and corresponding indexing employing bloom filters and binary search trees.…”

Section: Related Workmentioning

confidence: 99%

“…It also focused on using arbitrary permutations in iris code rows and corresponding indexing employing bloom filters and binary search trees. e inconsistency, irreversibility, and renewability have been experimentally demonstrated [24]. e issue of unavoidable biometric error rates using a unique cuckoo filtering solution is overcome that combines both steady bits and discriminatory bits at the same time to obtain a bigger and more powerful template protection system [25].…”

Section: Related Workmentioning

confidence: 99%

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Robust Authentication System with Privacy Preservation of Biometrics

Patil¹,

Raut²,

Jhaveri

et al. 2022

Security and Communication Networks

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IoT-based multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification using sensors. The leakage of the biometric trait information may cause critical privacy and security issues. It is expected to protect the privacy details of individuals through the irreversibility, unlinkability, and renewability of multi-biometric templates used in the authentication system. This study presents a robust authentication system with secure multi-biometric template protection techniques based on discrete cosine transform feature transformation and Lagrange’s interpolation-based image transformation. Three biometric traits namely iris, fingerprint, and palm print are recorded using sensors to validate the proposed multi-biometric template protection system. The fusion of all traits used is giving an average of 95.42% genuine acceptance rate and an average of 4.57% false rejection rate. Despite any number of biometric templates used for authentication, the proposed image transformation techniques keep the size of the final storage requirement as 8 X 8, which achieves constant space complexity (O(1)). The stored template is not linked with original templates; it is irreversible and renewable as new enrolment of the same individual will produce a new template every time. Overall, the proposed technique provides a secure authentication system with high accuracy, a constant size database, and the privacy preservation of biometric traits.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust Authentication System with Privacy Preservation of Biometrics

Patil¹,

Raut²,

Jhaveri

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…More recently, Rathgeb et al [18], [19] proposed the Bloom filter approach for alignment-free template cancelability and irreversibility. This approach was later adapted by Gomez-Barrero et al [9], [20] to ensure template nonlinkability, and by Drozdowski et al [21] for higher computational efficiency. Raja et al [22] proposed a highly efficient method using neighborhood-preserving manifolds and hashing for biometric template protection in smartphones.…”

Section: Background and Related Workmentioning

confidence: 99%

Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics

Pinto

Correia

Cardoso

2021

IEEE Trans. Biom. Behav. Identity Sci.

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Biometric systems store sensitive personal data that need to be highly protected. However, state-of-the-art template protection schemes generally consist of separate processes, inspired by salting, hashing, or encryption, that limit the achievable performance. Moreover, these are inadequate to protect current state-of-the-art biometric models as they rely on end-to-end deep learning methods. After proposing the Secure Triplet Loss, focused on template cancelability, we now reformulate it to address the problem of template linkability. Evaluated on biometric verification with off-the-person electrocardiogram (ECG) and unconstrained face images, the proposed method proves successful in training secure biometric models from scratch and adapting a pretrained model to make it secure. The results show that this new formulation of the Secure Triplet Loss succeeds in optimizing end-to-end deep biometric models to verify template cancelability, non-linkability, and non-invertibility.

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“…Some early proof-of-concept works and trade-off analyses have been carried out e.g. in [193][194][195].…”

Section: Data Securitymentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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Computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, a taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

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Privacy-Preserving Indexing of Iris-Codes with Cancelable Bloom Filter-based Search Structures

Cited by 19 publications

References 23 publications

Robust Authentication System with Privacy Preservation of Biometrics

Robust Authentication System with Privacy Preservation of Biometrics

Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics

Computational workload in biometric identification systems: an overview

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