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In recent literature, privacy protection technologies for biometric templates were proposed. Among these is the so-called helper-data system (HDS) based on reliable component selection. In this paper we integrate this approach with face biometrics such that we achieve a system in which the templates are privacy protected, and multiple templates can be derived from the same facial image for the purpose of template renewability. Extracting binary feature vectors forms an essential step in this process. Using the FERET and Caltech databases, we show that this quantization step does not significantly degrade the classification performance compared to, for example, traditional correlation-based classifiers. The binary feature vectors are integrated in the HDS leading to a privacy protected facial recognition algorithm with acceptable FAR and FRR, provided that the intra-class variation is sufficiently small. This suggests that a controlled enrollment procedure with a sufficient number of enrollment measurements is required.

Abstract. Physically Unclonable Functions (PUFs) are security primitives that exploit intrinsic random physical variations of hardware components. In the recent years, many security solutions based on PUFs have been proposed, including identification/authentication schemes, key storage and hardware-entangled cryptography. Existing PUF instantiations typically exhibit a static challenge/response behavior, while many practical applications would benefit from reconfigurable PUFs. Examples include the revocation or update of "secrets" in PUF-based key storage or cryptographic primitives based on PUFs. In this paper, we present the concept of Logically Reconfigurable PUFs (LR-PUFs) that allow changing the challenge/response behavior without physically replacing or modifying the underlying PUF. We present two efficient LR-PUF constructions and evaluate their performance and security. In this context, we introduce a formal security model for LR-PUFs. Finally, we discuss several practical applications of LR-PUFs focusing on lightweight solutions for resource-constrained embedded devices, in particular RFIDs.

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The present paper addresses privacy issues in electronic audio/video content distribution. It introduces an identitybased rights distribution and management system that enables users to access content anytime, anywhere, and on any device by means of authorization certificates issued by a content provider. These certificates openly link the identity of the users to the content that they are entitled to access. This fact, together with the availability of the certificates everywhere in the network, raises user privacy issues. A solution is proposed which deals with these issues and still allows the device to securely check the user's entitlement to the content.