Secure Sketches for Protecting Biometric Templates

Sutcu, Yagiz; Memon, Nasir

doi:10.1007/978-1-4471-5230-9_4

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…For example, multi-algorithm fusion at feature level, multibiometric cryptosystem fuzzy vault based on fingerprint and iris [51], fuzzy commitments for face [49] and other ideas for score fusion level were successfully applied to fingerprints with security advances and many other combinations under various scenarios have been proposed during the last three years [23], [51]. The target is to provide a uniform distribution of errors [30], combining successfully the data and covering research gaps of previous works, and thus, contributing to secure, stable systems [25], [54], while offering, a fast comparison of protected templates suitable for biometric recognition in identification mode.…”

Section: Multibiometric Template Protectionmentioning

confidence: 99%

“…In conclusion, in order to avoid fraud, privacy leakage should be decreased and the major requirement of unlinkability must be met. Furthermore, the alignment affects the recognition performance, the absence of a unified architecture brings confusion across the applications [25] and the desired properties for errorcorrection codes remain unattained.…”

Section: Biometric Template Protection Technologiesmentioning

confidence: 99%

“…Experiments on protected biometric data [42] lead to the assumption that the low boundaries between the false rejection rates and the error-correction capacities compound a more vulnerable system and at the same time, the requirements for stable biometric features are, definitely, non-trivial. Some of the approaches show that the protocols in the literature do not secure the encoding procedures [25] while others provide multiple suggestions for the distribution of reliability, or concentrate the efforts to the improvement of recognition rates [34]. With respect to the different multimodal biometrics template protection schemes, the interesting side contains the concentrated trials for a generic framework, focused on unified representation of biometric features, under the combination of the suggested protection designs.…”

Section: Combination Of Cryptography and Multimodalitiesmentioning

confidence: 99%

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A Survey on Multimodal Biometrics and the Protection of Their Templates

Toli

Preneel

2015

IFIP Advances in Information and Communication Technology

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Multibiometric Template Protectionmentioning

confidence: 99%

Section: Biometric Template Protection Technologiesmentioning

confidence: 99%

Section: Combination Of Cryptography and Multimodalitiesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Multimodal Biometrics and the Protection of Their Templates

Toli

Preneel

2015

IFIP Advances in Information and Communication Technology

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“…Owing to plenty of additive noise common during measurement, hashing the template is counter-productive [199]. There are many template protection methods like secure sketch schemes whose strength is measured by the average min-entropy of the original template given the secure sketch [200], fuzzy commitment scheme based on binary error-correcting codes [108], and the use of mutual information to measure dishonesty among users [45]. The Information Technology Laboratory (ITL) of the National Institute of Standards and Technology (NIST) recommends standards for biometric data exchange, system accuracy and interoperability [213].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

2019

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Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design. A. Sundararajan et al. • TBS consider each modality of the user as a separate entity while WBS consider the entire user (along with all of their individual modalities) as one [152] • TBS can be optionally connected to the Internet, while WBS are inherently online, exploiting the principles of Internet of Everything (IoE) [178] • The authentication and identification processes for TBS are static and user-initiated while for WBS are dynamic and autonomous

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“…Once a user's face template is compromised, the system cannot provide service to the user anymore. Numerous techniques have been proposed to protect biometrics template recently, which can be generally categorised as feature transformation [8–11] and secure sketch [12, 13] approaches. Biohashing [14, 15], cancellable biometrics [16–19], and robust hashing [20] are well‐known feature transformation approaches which usually use a non‐invertible or one‐way function to protect face templates.…”

Section: Introductionmentioning

confidence: 99%

Face template protection using deep LDPC codes learning

et al. 2019

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There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users' privacy. Here, the authors propose a face template protection technique by using multi-label learning, which maps the facials into low-density parity-check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi-label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra-variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

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Secure Sketches for Protecting Biometric Templates

Cited by 5 publications

References 24 publications

A Survey on Multimodal Biometrics and the Protection of Their Templates

A Survey on Multimodal Biometrics and the Protection of Their Templates

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Face template protection using deep LDPC codes learning

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