Protection of Privacy in Biometric Data

Natgunanathan, Iynkaran; Mehmood, Abid; Xiang, Yong; Beliakov, Gleb; Yearwood, John

doi:10.1109/access.2016.2535120

Cited by 76 publications

(25 citation statements)

References 73 publications

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“…Though a large number of methods for iris template protection have been proposed, many of them cannot satisfy the irreversibility, revocability, and unlinkability simultaneously while maintaining the recognition performance [2][3][4]. Therefore, we propose an iris template protection method based on local ranking, which satisfies the irreversibility, revocability, and unlinkability while maintaining the recognition performance.…”

Section: Related Workmentioning

confidence: 99%

“…Cancelable iris biometrics are mainly based on noninvertible transforms, which should maintain the similarity evaluation in the transformed domain. However, it is demonstrated that, in recent years, many existing methods for iris template protection could not satisfy the irreversibility, revocability, and unlinkability simultaneously, while maintaining the recognition performance [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Iris Template Protection Based on Local Ranking

Zhao

Fang

Xiang

et al. 2018

Security and Communication Networks

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Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iris Template Protection Based on Local Ranking

Zhao

Fang

Xiang

et al. 2018

Security and Communication Networks

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“…However, biometric information itself is sensitive and has an inherent problem that it cannot be changed if leaked. Therefore, it needs to be closely guarded (Natgunanathan et al, 2016 ). Biometric authentication still suffers from problems of accuracy and cost, although it has improved recently.…”

Section: Authentication Methodsmentioning

confidence: 99%

Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness

2018

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Genome and other data are already being used in areas including cancer and rare diseases. Data-sharing and secondary uses are likely to become much broader and far more extensive; thus, obtaining proper consent for these new uses of data is an important issue. Obtaining consent through online methods may be an option to overcome the problems associated with one-off, paper-based informed consent. When the process of obtaining consent takes place remotely, authentication must be assured. Patients may also choose to store some of their own information online, such as genetic information, and allow healthcare professionals to access this data. In this health information transfer and exchange process, it is vital that anyone accessing this information be correctly authenticated to protect patients' privacy. In this article, we first clarified that authentication has two roles: i.e., not only to prevent impersonation but also to prove intent, which is a vital step to ensure that medical research and health information exchange are conducted ethically. We then set out methods of authentication. As a result, we were able to make suggestions about the requirements for authentication and a possible method of authentication for these purposes. We considered problems of biometrics and recommended two-factor authentication without biometrics as a workable solution. However, three-factor authentication including biometrics seems likely to be used once biometrics become more common.

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“…A number of aspects of privacy protection has been studied in the biometric literature [19,24,21,18]. On one hand, there are face de-identification techniques [11,20,7] where a face image is modified in order to confound a face matcher.…”

Section: Related Workmentioning

confidence: 99%

Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

Mirjalili

Raschka

Namboodiri

et al. 2018

2018 International Conference on Biometrics (ICB)

106

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In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semiadversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of an auxiliary gender classifier and an auxiliary face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.

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Protection of Privacy in Biometric Data

Cited by 76 publications

References 73 publications

Iris Template Protection Based on Local Ranking

Iris Template Protection Based on Local Ranking

Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness

Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

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