Cancelable Biometrics: A review

Patel, Vishal M.; Ratha, Nalini K.; Chellappa, Rama

doi:10.1109/msp.2015.2434151

Cited by 340 publications

(136 citation statements)

References 65 publications

(126 reference statements)

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“…Several decent and comprehensive review papers exist in this topic such as [2] [5] [6]. We direct the readers to explore the details from these review papers.…”

Section: Literature Reviewmentioning

confidence: 99%

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Jin

Hwang

Lai

et al. 2018

IEEE Trans.Inform.Forensic Secur.

175

101

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Abstract-In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy several merits. Firstly, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.Index Terms-Fingerprint, cancelable template, Index-of-Max hashing, security and privacy I. INTRODUCTIONATELY, rapid proliferation of biometric applications leads to massive amount of biometric templates. Public worries about the security and privacy of the biometric templates if stolen or compromised. Such concerns are attributed to the strong binding of individuals and privacy, and further complicated by the fact that biometric traits are irrevocable. Given the above threats, a number of proposals have been reported to protect the biometric templates. Generally, the proposals available in the literature can be broadly divided into two categories: feature transformation (or cancelable biometrics) and biometric cryptosystems (biometric encryption). Biometric cryptosystem serves the purpose of either securing the secret using biometric feature (key binding) or generating the secret directly from the biometric feature (key generation). On the other hand, cancelable biometrics [1] is truly meant for biometric template protection. It refers to the irreversible transform that can alter the biometric templates such that security and privacy of the templates can be assured. If a cancelable template is compromised, a new template can be re-generated from the same biometrics.The cancelable biometric schemes in the literature vary according to different biometric modalities. However, the operation of a general cancelable biometrics system is similar to the conventional biometric system where the system composes of sensor, feature extractor and matcher except the former includes a parameterized transformation function right after feature extractor, and the matching is done in the transformed domain, rather in the feature doma...

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“…Several decent and comprehensive review papers exist in this topic such as [2] [5] [6]. We direct the readers to explore the details from these review papers.…”

Section: Literature Reviewmentioning

confidence: 99%

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Jin

Hwang

Lai

et al. 2018

IEEE Trans.Inform.Forensic Secur.

175

101

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“…Cancellable biometric includes cancellable face [15], fingerprint [16], iris [17], voice [18] and other biometric modalities. A detailed and comprehensive overview of cancellable biometrics and biometric cryptosystems is given in [19,20].…”

Section: Privacymentioning

confidence: 99%

De-identification for privacy protection in multimedia content: A survey

Ribarić

Ariyaeeinia

Pavešić

2016

Signal Processing: Image Communication

152

107

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a b s t r a c tPrivacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. Deidentification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents.

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“…Feature extraction is an important step in speech retrieval, with most research making use of perceptual hashes [7][8][9] or audio fingerprints [10][11][12]. In addition, in order to improve system security, some good methods have been applied, such as zero-leakage biometric protection [13], cancelable biometrics [14,15], biometrics hashing [16,17], and secret key generation [18].…”

Section: Introductionmentioning

confidence: 99%

An efficient retrieval algorithm of encrypted speech based on inverse fast Fouriertransform and measurement matrix

Zhang¹,

Ge²,

Zhou³

et al. 2019

Turk J Elec Eng & Comp Sci

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In this paper, we present an efficient retrieval algorithm for encrypted speech based on an inverse fast Fourier transform and measurement matrix. Our approach improves query performance, as well as retrieval efficiency and accuracy, compared to existing content-based encrypted speech retrieval methods. Our proposed algorithm constructs a perceptual hash scheme using perceptual hash sequences from original speech files. By classifying the sequences and applying run-length compression, we decrease the cloud storage required for the hash index. We secure the speech database by encrypting it with Henon chaos scrambling, which offers excellent resistance to attacks. Experimental results show that the robustness, discrimination, and feature extraction efficiency of our proposed method are better than the existing alternatives, with good recall and precision ratios and with high retrieval efficiency and accuracy.

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Cancelable Biometrics: A review

Cited by 340 publications

References 65 publications

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

De-identification for privacy protection in multimedia content: A survey

An efficient retrieval algorithm of encrypted speech based on inverse fast Fouriertransform and measurement matrix

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