Universal hashing and authentication codes
Designs, Codes and Cryptography
A Pseudorandom Generator from any One-way Function
SIAM Journal on Computing
Universal Hashing and Authentication Codes
CRYPTO '91 Proceedings of the 11th Annual International Cryptology Conference on Advances in Cryptology
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
Security with Noisy Data: Private Biometrics, Secure Key Storage and Anti-Counterfeiting
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
SIAM Journal on Computing
FPGA Intrinsic PUFs and Their Use for IP Protection
CHES '07 Proceedings of the 9th international workshop on Cryptographic Hardware and Embedded Systems
A Computational Introduction to Number Theory and Algebra
A Computational Introduction to Number Theory and Algebra
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
Read-proof hardware from protective coatings
CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
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Extraction of uniform randomness from (noisy) non-uniform sources is an important primitive in many security applications, e.g. (pseudo-)random number generators, privacy-preserving biometrics, and key storage based on Physical Unclonable Functions. Generic extraction methods exist, using universal hash functions. There is a trade-off between the length of the extracted bit string and the uniformity of the string. In the literature there are proven lower bounds on this length as a function of the desired uniformity. The best known bound involves a quantity known as smooth min-entropy. Unfortunately, there exist at least three definitions of smooth entropy. In this paper we compare three of these definitions, and we derive improved lower bounds on the extractable randomness. We also investigate the use of almost universal hash functions, which are slightly worse at extracting randomness than universal hash functions, but are preferable in practice because they require far less resources in devices. We show that using them has negligible effect on the extractable randomness.