Jean-Marie Le Bars

Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.

The M{\"o}bius transform is a crucial transformation into the Boolean world; it allows to change the Boolean representation between the True Table and Algebraic Normal Form. In this work, we introduce a new algebraic point of view of this transformation based on the polynomial form of Boolean functions. It appears that we can perform a new notion: the M{\"o}bius computation variable by variable and new computation properties. As a consequence, we propose new algorithms which can produce a huge speed up of the M{\"o}bius computation for sub-families of Boolean function. Furthermore we compute directly the M{\"o}bius transformation of some particular Boolean functions. Finally, we show that for some of them the Hamming weight is directly related to the algebraic degree of specific factors.

Template protection is an important issue in biometrics for security and privacy reasons. One solution for securing fingerprint data is to store it on a Secure Element (a microcircuit chipset such as a smartcard). An embedded On-Card-Comparison (OCC) module permits to compare two biometric templates and generates a similarity score. The biometric template is usually composed of minutiae extracted from the fingerprint image because a Secure element is limited in terms of memory and computation capabilities. For these reasons, a template reduction is necessary to quickly process fingerprint comparison. In this paper, we propose a new fingerprint template reduction scheme by approximating the optimal choice of minutiae with a genetic algorithm. We compared the proposed method with approaches from the literature using a fingerprint dataset and three matching algorithms. The experimental results show the benefit of the proposed method especially in order to estimate the optimal pe

In this paper, we deal with the proof of ownership or legitimate usage of a digital content, such as an image, in order to tackle the illegitimate copy. The proposed scheme based on the combination of the watermark-ing and cancelable biometrics does not require a trusted third party, all the exchanges are between the provider and the customer. The use of cancelable biometrics permits to provide a privacy compliant proof of identity. We illustrate the robustness of this method against intentional and unintentional attacks of the watermarked content.