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—In this paper we analyze secret generation in biomet-ric identification systems with protected templates. This problem is closely related to the study of the biometric identification capacity of Willems et al. 2003 and O'Sullivan and Schmid 2002 and the common randomness generation of Ahlswede and Csiszár 1993. In our system two terminals observe biometric(More)
—This paper addresses privacy leakage in biometric secrecy systems. Four settings are investigated. The first one is the standard Ahlswede–Csiszár secret-generation setting in which two terminals observe two correlated sequences. They form a common secret by interchanging a public message. This message should only contain a negligible amount of information(More)
— We propose methods to estimate the secrecy-rate of fuzzy sources (e.g. biometrics and Physical Unclonable Functions (PUFs)) using context-tree weighting (CTW, Willems et al. [1995]). In this paper we focus on PUFs. In order to show that our estimates are realistic we first generalize Maurer's [1993] result to the ergodic case. Then we focus on the fact(More)
—In 1999, Juels and Wattenberg introduced the fuzzy commitment scheme. This scheme is a particular realization of a binary biometric secrecy system with chosen secret keys. It became a popular technique for designing biometric secrecy systems, since it is convenient and easy to implement using standard error-correcting codes. This paper investigates(More)
Virtual organizations are dynamic, inter-organizational collaborations that involve systems and services belonging to different security domains. Several solutions have been proposed to guarantee the enforcement of the access control policies protecting the information exchanged in a distributed system, but none of them addresses the dynamicity(More)
—Fuzzy commitment of Juels and Wattenberg 1999 is a popular technique for designing secure systems based on noisy data. The scheme is easy to implement using standard error-correcting codes. However, secrecy of this scheme is only guaranteed when input data are generated by uniform i.i.d. sources, while typical input data (PUFs and biometrics) are not(More)