Supplemental Access Control (PACE v2): Security Analysis of PACE Integrated Mapping

We describe and analyze the password-based key establishment protocol PACE v2 Integrated Mapping (IM), an evolution of PACE v1 jointly proposed by Gemalto and Sagem Sécurité. PACE v2 IM enjoys the following properties: patent-freeness3 (to the best of current knowledge in the field); full resistance to dictionary attacks, secrecy and forward secrecy in the security model agreed upon by the CEN TC224 WG16 group; optimal performances. The PACE v2 IM protocol is intended to provide an alternative to the German PACE v1 protocol, which is also the German PACE v2 Generic Mapping (GM) protocol, proposed by the German Federal Office for Information Security (BSI). In this document, we provide a description of PACE v2 IM, a description of the security requirements one expects from a password-based key establishment protocol in order to support secure applications, and a security proof of PACE v2 IM in the so-called Bellare-Pointcheval-Rogaway (BPR) security model

The Cryptographic Security of the German Electronic Identity Card

In November 2010, the German government started to issue the new electronic identity card (eID) to its citizens. Besides its original utilization as a ’visual’ identification document, the eID card can be used by the cardholder to prove one’s identity at border control and to enhance security of authentication processes over the Internet, with the eID card serving as a token to reliably transmit personal data to service providers or terminals, respectively. To this end, the German Federal Office for Information Security (BSI) proposed several cryptographic protocols now deployed on the eID card. The Password Authenticated Connection Establishment (PACE) protocol secures the wireless communication between the eID card and the user’s local card reader, based on a cryptographically weak password like the PIN chosen by the card owner. Subsequently, the Extended Access Control (EAC) protocol is executed by the chip and the service provider to mutually authenticate and agree on a shared secret session key. This key is then used in the secure channel protocol, called Secure Messaging (SM). Finally, an optional protocol, called Restricted Identification (RI), provides a method to use pseudonyms such that they can be linked by individual service providers, but not across different service providers (even not by malicious ones). This thesis consists of two parts. First, we present the above protocols and provide a rigorous analysis on their security from a cryptographic point of view. We show that the Germen eID card provides reasonable security for authentication and exchange of sensitive information allaying concerns regarding its usage. In the second part of this thesis, we introduce two possible modifications to enhance the security of these protocols even further. Namely, we show how to (a) add to PACE an additional efficient chip authentication step, and (b) augment RI to allow also for signatures under pseudonyms

Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Enhancing Privacy Protection:Set Membership, Range Proofs, and the Extended Access Control

Privacy has recently gained an importance beyond the field of cryptography. In that regard, the main goal behind this thesis is to enhance privacy protection. All of the necessary mathematical and cryptographic preliminaries are introduced at the start of this thesis. We then show in Part I how to improve set membership and range proofs, which are cryptographic primitives enabling better privacy protection. Part II shows how to improve the standards for Machine Readable Travel Documents (MRTDs), such as biometric passports. Regarding set membership proofs, we provide an efficient protocol based on the Boneh-Boyen signature scheme. We show that alternative signature schemes can be used and we provide a general protocol description that can be applied for any secure signature scheme. We also show that signature schemes in our design can be replaced by cryptographic accumulators. For range proofs, we provide interactive solutions where the range is divided in a base u and the u-ary digits are handled by one of our set membership proofs. A general construction is also provided for any set membership proof. We additionally explain how to handle arbitrary ranges with either two range proofs or with an improved solution based on sumset representation. These efficient solutions achieve, to date, the lowest asymptotical communication load. Furthermore, this thesis shows that the first efficient non-interactive range proof is insecure. This thesis thus provides the first efficient and secure non-interactive range proof. In the case of MRTDs, two standards exist: one produced by the International Civil Aviation Organization (ICAO) and the other by the European Union, which is called the Extended Access Control (EAC). Although this thesis focuses on the EAC, which is supposed to solve all privacy concerns, it shows that both standards fail to provide complete privacy protection. Lastly, we provide several solutions to improve them

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC