A Fuzzy Vault Scheme

By Ari Juels, Madhu Sudan. Proceedings IEEE International Symposium on Information Theory,, 2002

1,676 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b3d128ddb7f21cb5be06c023eae5143cfc03db5c

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Citations in the corpus

Reusable cryptographic fuzzy extractors (2004, background): “ber of constructions geared toward specific applications have surfaced in the last few years [5, 11, 12, 10].”
Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs (2006, methodology): “This paper presents a method that conforms to these cancellability criteria.”
An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification (2010, methodology): “[12] A. Juels and M. Sudan, “A fuzzy vault scheme,” in Proc.”
Cracking Fuzzy Vaults and Biometric Encryption (2007, methodology): “In order to solve the problem of hiding a key and unlocking it with biometric or other approximately matching data, Juels and Sudan [5] introduce a construct called a fuzzy vault.”
Fuzzy Vault for Fingerprints (2005, methodology): “Clancy et al. [8] proposed a fingerprint vault based on the fuzzy vault of Juels and Sudan [4] .”
Correcting errors without leaking partial information (2005, background): “First, W itself may be sensitive (say, if it is a biometric used for authentication [15, 16, 11]), in which case S(W ) might reveal sensitive information, such as a person’s age.”
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data (2004, background): “Similarly, a long pass-phrase (or answers to 15 questions [FJ01] or a list of favorite movies [JS06]) is not uniformly random and is difﬁcult to remember for a human user.”
Secure Face Matching Using Fully Homomorphic Encryption (2018, background): “Fuzzy vault [19] based approaches have been proposed for biometric authentication in the context of fingerprint [47] and iris [22] patterns.”
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.”
Multibiometric Cryptosystems Based on Feature-Level Fusion (2012, background): “Date of publication October 03, 2011; date of current version January 13, 2012.”
Generating Cancelable Fingerprint Templates (2007, background): “Further details may be found in [7], [10], [11], [13], [28], [29].”
Cancelable Biometrics: A review (2015, background): “Examples of key binding systems include fuzzy commitment [11] and fuzzy vault [6].”
Biometric Template Protection: Bridging the performance gap between theory and practice (2015, neutral): “Such databases are commonplace in large-scale identification systems (e.g., India’s Aadhaar program, Office of Biometric Identity Management (formerly USVISIT) program).”
Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition (2010, methodology): “Unfortunately, designing effective template protection techniques (e.g., fuzzy vault [ 40 ], [41], [42], [43], [44]) without incurring a relevant accuracy drop is very challenging and seems to require alignment free, fixed-length, and noise-tolerant feature coding.”
Location Privacy via Private Proximity Testing (2011, methodology): “Juels and Sudan construct “fuzzy vaults” which is a related primitive [24].”
Fingerprint-Based Fuzzy Vault: Implementation and Performance (2007, methodology): “Fuzzy vault [7] is a cryptographic construction that is designed to work with biometric features which are represented as an unordered set (e.g., minutiae in fingerprints).”