Secure Processing of Biometric Signals in Malicious Setting

In the digital and interconnected world we live in, establishing the identity of any individual is a pressing need. Home banking, on line shopping, and social care web sites are only few examples of services where proof of identity is fundamental. Such a process can be based on "what you know" (i.g. a password), on"what you posses" (i.g. the key of a house or an ID card) or on "what you are"(ID-based, i.g. biometrics). In this thesis we focus on biometrics. Biometric recognition, or simply biometrics, refers to ``the automated recognition of individuals based on behavioral and biological characteristics'' (ISO/IEC JTC1 SC37). This method of recognition has the advantage that it does not need the memorization of any password or the possess of any token, at the same time, however, biometrics cannot be changed if compromised in any way, hence calling for the adoption of suitable protection mechanisms. In this thesis we study the development of privacy preserving protocols for biometric recognition. This is a new research field for which a number of solutions have been proposed in recent years. For efficiency reasons, the majority of those solutions are secure only against a passive adversary, that is an adversary that does not deviate from the protocol, yet tries to infer as much information as possible from the data exchanged during the protocol. On the contrary, in this thesis we look for protocols which are secure against active adversaries, that is adversaries that deliberately and arbitrarily deviate from the recognition protocol. Specifically, we propose two possible solutions using signal processing in the encrypted domain's tools. First we use a cryptographic scheme belonging to the somewhat homomorphic scheme's family and we propose both an identification and an authentication non-interactive scheme. The first protocol focuses on a one-to-many recognition task: the biometric probe of a specific individual is compared with all the probes contained in a database looking for a positive match. The second protocol, instead, considers a one to one comparison. The new probe of an enrolled individual is compared with the probe of the same individual stored during the enrollment phase. As a second contribution, we propose SEMBA: a protocol secure against active adversary for multibiometric recognition. In this case we look for a trade-off between efficiency and accuracy by combining information from two biometric traits instead of only one. The protocol relies on SPDZ, a new framework proposed by Damgård et al. which is secure also in the presence of an active adversary.