In this correspondence, we analyze the vulnerabilities of biometric authentication protocols with respect to user and data privacy. The goal of an adversary in such context is not to bypass the authentication but to learn information either on biometric data or on users that are in the system. We elaborate our analysis on a general system model involving four logical entities (sensor, server, database, and matcher), and we focus on internal adversaries to encompass the situation where one or a combination of these entities would be malicious. Our goal is to emphasize that when going beyond the usual honest-but-curious assumption much more complex attacks can affect the privacy of data and users. On the one hand, we introduce a new comprehensive framework that encompasses the various schemes we want to look at. It presents a system model in which each internal entity or combination of entities is a potential attacker. Different attack goals are considered and resulting requirements on data flows are discussed. On the other hand, we develop different generic attacks. We follow a blackbox approach in which we consider components that perform operations on biometric data but where only the input/output behavior is analyzed. These attack strategies are exhibited on recent schemes such as the distributed protocol of Bringer (ACISP 2007), which is based on the Goldwasser-Micali cryptosystem, the related protocol of Barbosa (ACISP 2008), which uses the Paillier cryptosystem, and the scheme of Stoianov (SPIE 2010), that features the Blum-Goldwasser cryptosystem. All these schemes have been developed in the honest-but-curious adversary model and show potential weaknesses when considered in our malicious insider attack model.

The increasing use of biometrics has given rise to new privacy concerns. Biometric encryption systems have been proposed in order to alleviate such concerns: rather than comparing the biometric data directly, a key is derived from these data and subsequently knowledge of this key is proved. One specific application of biometric encryption is the use of biometric sketches: in this case biometric template data are protected with biometric encryption. We address the question whether one can undermine a user's privacy given access to biometrically encrypted documents, and more in particular, we examine if an attacker can determine whether two documents were encrypted using the same biometric. This is a particular concern for biometric sketches that are deployed in multiple locations: in one scenario the same biometric sketch is deployed everywhere; in a second scenario the same biometric data is protected with two different biometric sketches. We present attacks on template protection schemes that can be described as fuzzy sketches based on error-correcting codes. We demonstrate how to link and reverse protected templates produced by code-offset and bit-permutation sketches.

In this paper we present a scheme for building a logging- trail for processes related to eGovernment services. A citizen can reconstruct the trail of such a process and verify its status if he is the subject of that process. Reconstruction is based on hand-overs, special types of log events, that link data stored by multiple logging servers, which are not necessarily trusted. Our scheme is privacy-friendly in the sense that only the authorised subject, i.e. the citizen, can link the different log entries related to one specific process. The scheme is also auditable; it allows logging servers to show that they behave according to a certain policy.