Cancelable Biometrics: a comprehensive survey

Manisha, .; Kumar, Nitin

doi:10.1007/s10462-019-09767-8

Cited by 100 publications

(42 citation statements)

References 141 publications

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“…The privacy-issue in biometrics can also be seen from the perspective of cancelable biometrics. Similar to soft-biometric privacy, cancelable biometrics approaches apply one-way functions to transform biometric data [1], [23], [32] and store the transformed data [4]. However, the solutions from both areas target different goals.…”

Section: Related Workmentioning

confidence: 99%

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

et al. 2020

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Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

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Section: Related Workmentioning

confidence: 99%

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

et al. 2020

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“…Lack of standard performance measures: There are several measures that have been used to assess the performance of a biometric system. Due to the lack of a single measure to quantify the system, it is very hard to make a comparison of different methods that have been published in the literature (Ryu et al, 2021;Kumar, Prasad & Raju, 2020;Manisha & Kumar, 2020). There is a need to work on a unified measure that is widely adopted by the researchers to evaluate different authentication systems, and making the literature standardized and valid for performing direct comparisons.…”

Section: Open Challenges and Future Directions Challengesmentioning

confidence: 99%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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“…Template protection has been approached from many different angles. The different methods can be classified into the following main categories [26]: a) Cryptosystems: cryptographic keys are derived from or bound to the biometric information. Some relevant methods are BioHashing [27] and Fuzzy Extractors [28], which will be further explained in the next Section.…”

Section: B On Template Protectionmentioning

confidence: 99%

On the Security of Biometrics and Fuzzy Commitment Cryptosystems: A Study on Gait Authentication

hamme¹,

Rúa

Preuveneers

et al. 2021

IEEE Trans.Inform.Forensic Secur.

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As biometric templates consist of highly correlated features, the real security level offered by biometric authentication systems remains an open research question. In this work we provide new approximations and a lower bound of the security offered by fuzzy commitment schemes. Fuzzy commitment cryptosystems and in general biometric template protection schemes play an important role in allowing for remote storage and processing of biometric data, as they mitigate the threat of biometric template leakage. The use of such schemes would alleviate some of the usability constraints imposed by the state-ofpractice local use of biometrics. As such we conduct an in-depth security analysis for IMU based gait authentication systems, where we evaluate the effectiveness of attacks within the scope of two well-defined threat models that target both unprotected and protected systems. A pivotal enabler of our analysis is the development of nine different approaches to gait authentication, which allows us to perform intramodal fusion on these distinct, yet highly correlated biometric templates, and to protect them with a strengthened fuzzy commitment scheme. Our analysis clearly demonstrates the high correlation between the different biometric templates, which, among others, further showcases the threat of biometric template leakage. Furthermore, as our analysis incorporates a threat model that assumes biometric template leakage, it provides metrics for the security provided by the biometric modality itself.

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Cancelable Biometrics: a comprehensive survey

Cited by 100 publications

References 141 publications

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

Hand-based multibiometric systems: state-of-the-art and future challenges

On the Security of Biometrics and Fuzzy Commitment Cryptosystems: A Study on Gait Authentication

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