Enhancing security and privacy in biometrics-based authentication systems

By Nalini K. Ratha, J. Connell, Ruud M. Bolle. IBM Systems Journal, 2001

1,830 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34c79bec4bd785bc65e885a3a992e692cdc76126

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Citations in the corpus

Multi-Factor Authentication: A Survey (2018, background): “This was well studied over past decades in classical statistical decision theory from the authentication perspective [69,70].”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, background): “Several weak links and vulnerabilities are identiﬁed by Ratha et al. in [Ratha et al. 2001] (see Fig.”
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images (2017, background): “A number of aspects of privacy protection has been studied in the biometric literature [19, 24, 21, 18].”
Comparing passwords, tokens, and biometrics for user authentication (2003, background): “However, biometric features can be copied or counterfeited—with varying levels of difficulty—and used to gain unauthorized access to a security system [21]–[23].”
Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification (2017, background): “Unfortunately, however, and as is true for all biometric modalities, concerns regarding security and privacy vulnerabilities (Ratha et al., 2001; Alice, 2003; Campisi, 2013) can still form a barrier to exploitation.”
Guide to Biometrics (2003, methodology): “Another type of challenge and response to detect replay attacks in hardware is proposed in [181].”
Attacks on biometric systems: a case study in fingerprints (2004, methodology): “For eliminating type 2 attacks, where a previously intercepted biometric is replayed, Ratha et al. [9] proposed a challenge/response based system.”
Spoofing and countermeasures for speaker verification: A survey (2015, background): “A generic biometric system may be manipulated or attacked at various stages between sample acquisition and the delivery of an authentication result (Ratha et al., 2001; Faundez-Zanuy, 2004; Galbally et al., 2010).”
Privacy-Preserving Biometric Identification Using Secure Multiparty Computation: An Overview and Recent Trends (2013, methodology): “The previous techniques for preserving the privacy of biometric data while maintaining their usability in recognition systems, such as fuzzy vault [3], fuzzy sketches [4], or cancelable biometrics [5] are based on encoding techniques.”
Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures (2008, neutral): “Keywords- Curvelets, Fingerprints, Liveness, Ridgelets, Texture features, Wavelets.”
Gabor Filter-Based Fingerprint Anti-spoofing (2008, background): “Trojan horse attack on feature extractor or the matcher), replay attacks, modifying enrolled templates, attacking channel between template databases and matching module, etc [ 1 ].”
Face Recognition on Consumer Devices: Reflections on Replay Attacks (2015, background): “Users remain concerned over privacy [29] and the difficulty in revoking compromised biometric data [30].”
A survey on biometric cryptosystems and cancelable biometrics (2011, methodology): “The FRR of a BCS defines the rate of incorrect keys untruly generated by the system, that is, the percentage of incorrect keys returned to genuine users (correct keys are user-specific and associated with according helper data).”
Biometric perils and patches (2002, neutral): “[13].”