The project WISEPHY will investigate physical-layer security schemes for wireless communications, a new emerging paradigm for secure communications that builds its theoretical foundations on information- theoretic principles. The general aim is to demonstrate through theoretical models, code design and proof-of- principle experiments that there is much to be gained by coding for security at the physical layer of wireless links. In fact, the security mechanisms of most wireless communications protocols are implemented in the upper layers of the communications architecture, assuming that the physical-layer has already been estab- lished. In contrast, several information-theoretic results suggest that the randomness inherently present in a wireless communication medium (fading, thermal noise, interference) can be harnessed to conceal infor- mation from potential eavesdroppers by coding at the physical-layer itself. This approach has been dubbed “physical-layer security” and has attracted considerable interest lately in the information theory community. Unfortunately, the promises of information theory have not yet translated into practical engineering solutions. Although the fundamental limits of secure communications over noisy channels are now better understood, few practical coding schemes exist that guarantee any level of physical-layer security. The project WISEPHY aims at bridging the gap between information theory and engineering solutions by addressing several relevant aspects of physical-layer security. The originality of the project stems from a comprehensive “bottom-up” approach, which explores information theoretic, coding theoretic, experimental and cryptographic facets of the problem. In particular, two key aspects of the project that break new ground are the design, analysis and implementation of practical coding schemes, as well as the cryptanalysis of these coding schemes in realistic settings.

The project SAFAS (Self Complementary Structure with Low Signature) is proposed by a consortium composed of Telecom ParisTech, ONERA and SART. Objective is the study and development of a planar multilayer structure to achieve a low profile directive antenna array with low SER and frequency agile thin absorbent material. The novelty of this project is based on very specific properties of self-complementary structures that have a theoretical bandwidth unlimited and which, together with one or more high impedance surfaces, may give compact wideband directive antennas or thin and frequency agile absorbent materials. These composite absorbent structures can be used as ground plane for antenna when they’re without losses or as an absorbent material they’re with or without losses. In addition, the introduction of discrete elements and controllable in the high-impedance surface moves its resonant frequency and thus lead to the production of a reconfigurable absorbent material.

TRUST focuses on personal data protection measures to meet the objectives of the RGPD but also the texts in preparation such as the "Data Act" or the "Data Governance Act". We propose to study and develop new security solutions, based on advanced cryptography, for use cases involving the reuse of personal data. These use cases will present various configurations in terms of actors, type of data and processing, opening the way to different technical and legal issues. We thus seek to anticipate legal evolutions and prepare technical architectures to allow the reuse of personal data in compliance with the various legal frameworks.

The XAI4AML (Explainable AI for Anti-Money Laundering) chair will explore how AI and explainability affect the optimal level of financial regulation, including how different levels of explainability and regulation may affect the costs and benefits associated with deploying AI-based solutions for anti-money laundering (AML) enforcement. Traditional approaches used by banks to fight money laundering are both costly (€20 billion per year in Europe), and relatively ineffective, being based on deterministic rule-based models. Current AML systems generate many false positives, while at the same time missing large amounts of truly suspicious transactions. Professional criminals use sophisticated techniques to disguise transfers as normal-looking transactions. AI can reduce false positives and bring about greater effectiveness by identifying otherwise invisible trends across large data sets. However, problems of explainability, together with regulatory uncertainty, are the main barriers to implementing AI in AML systems. My interdisciplinary chair, combining economics, law (with Winston Maxwell, Director Law & Technology) and AI/data science (Stéphan Clémençon, Professor Applied Mathematics), will contribute to the economic literature on the economics of financial regulation and financial crime, while at the same time contributing to an operational need for clarity on what constitutes and “explainable” AI system for AML. The results will have a positive impact on designers of AI-based AML systems (such as the French fintech Bleckwen, partner of the chair), on users of the systems (such as banks and consulting firms, in particular PWC specialized in banking operations and compliance, partner of the chair), and on the financial regulator (ACPR, partner of the chair).