Secret-key reconciliation by public discussion
EUROCRYPT '93 Workshop on the theory and application of cryptographic techniques on Advances in cryptology
Fuzzy extractors for continuous distributions
ASIACCS '07 Proceedings of the 2nd ACM symposium on Information, computer and communications security
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
New shielding functions to enhance privacy and prevent misuse of biometric templates
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Secure sketch for biometric templates
ASIACRYPT'06 Proceedings of the 12th international conference on Theory and Application of Cryptology and Information Security
Read-proof hardware from protective coatings
CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
Reconciliation of a quantum-distributed Gaussian key
IEEE Transactions on Information Theory
IH'10 Proceedings of the 12th international conference on Information hiding
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We address the problem of designing optimal schemes for the generation of secure cryptographic keys from continuous noisy data. We argue that, contrary to the discrete case, a universal fuzzy extractor does not exist. This implies that in the continuous case, key extraction schemes have to be designed for particular probability distributions. We extend the known definitions of the correctness and security properties of fuzzy extractors. Our definitions apply to continuous as well as discrete variables. We propose a generic construction for fuzzy extractors from noisy continuous sources, using independent partitions. The extra freedom in the choice of discretization, which does not exist in the discrete case, is advantageously used to give the extracted key a uniform distribution. We analyze the privacy properties of the scheme and the error probabilities in a one-dimensional toy model with simplified noise. Finally, we study the security implications of incomplete knowledge of the source's probability distribution P. We derive a bound on the min-entropy of the extracted key under the worst-case assumption, where the attacker knows P exactly.