Vulnerabilities in Biometric Encryption Systems

By Andy Adler. 2005

168 citations recorded by the source corpus.

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

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Citations in the corpus

Security Evaluation of Pattern Classifiers under Attack (2014, background): “number of query samples that the adversary has to issue to violate privacy [14], [16], [41].”
An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification (2010, background): “Furthermore, the security level of such methods still needs to be further investigated [5], [6].”
Cracking Fuzzy Vaults and Biometric Encryption (2007, methodology): “Other blended substitution attacks combine the use of hillclimbing [ 2 ] to estimate a masquerade image M and key, which are then used as stand-ins for the known biometric/key in the above attacks.”
Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective (2015, background): “…users are denied access or normal system operation is compromised (e.g., misclassifying legitimate e-mails as spam); and a privacy violation, if the adversary is able to exploit confidential information about the system (e.g., the clients' templates in a biometric recognition system [10]–[12]).”
A survey on biometric cryptosystems and cancelable biometrics (2011, background): “[189] proposed a “hill-climbing” attack against the Biometric EncryptionTM algorithm in which a sample biometric input is iteratively modified while the internal comparison score is observed.”
Biometric Template Security (2008, background): “Insecure communication channels also allow an adversary to launch replay [28] or hill-climbing attacks [29].”
Security evaluation of biometric authentication systems under real spoofing attacks (2012, background): “Besides the so-called “indirect attacks”, which require some knowledge of the system or access to its internal parameters [32, 1, 13, 24], much more attention has been devoted to the so-called “direct attacks”, namely, attacks which consist of submitting a counterfeited biometric (i.e., a replica…”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “However, the presence of intelligent, adaptive adversaries who exploit their knowledge of the targeted system to evolve their attack strategy over time, introduces a further source of adversarial non-stationarity, that can be modeled by introducing a random variable A, as p(X,Y,A; t)[41, 20] (see…”
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018, methodology): “ models provided as a service; and model-inversion and hill-climbing attacks, aimed to steal sensitive information like the face and fingerprint templates of users of biometric authentication systems [1, 8, 22, 23, 33, 55]. 2With the term feature representation, we do not mean the internal represent”