This project aims to propose a declarative language dedicated to cryptanalytic problems in symmetric key cryptography using constraint programming (CP) to simplify the representation of attacks, to improve existing attacks and to build new cryptographic primitives that withstand these attacks. We also want to compare the different tools that can be used to solve these problems: SAT and MILP where the constraints are homogeneous and CP where the heterogeneous constraints can allow a more complex treatment. One of the challenges of this project will be to define global constraints dedicated to the case of symmetric cryptography. Concerning constraint programming, this project will define new dedicated global constraints, will improve the underlying filtering and solution search algorithms and will propose dedicated explanations generated automatically.

Biometric recognition refers to the automated recognition of individuals based on their physical, biological or behavioural characteristics. Since biometric characteristics cannot be lost or forgotten, biometric authentication solutions are in general prefered over their password or physical token counterparts. Although biometric solutions are more convenient and quicker to use, they are not exempt from vulnerabilities. If not well protected, they may actually be vulnerable to impersonation attacks and privacy leakage. Biometric data serves as a long-term and unique personal identifier, hence categorized as a highly sensitive personal data, falling under the scope of the General Data Protection Regulation (GDPR). As biometric-based technologies are deployed at a larger scale, biometric databases and devices become natural targets in cyber attacks. These cyberattacks have the potential to be harmful on the long term if they lead to the theft of biometric data. Furthermore, the biometric identifiers of individuals should be transmitted to service providers in a form that prevents cross-matching across databases. Privacy is a fundamental right which is now enforced through the GDPR. Our project PRIVABIO will contribute to both the security of biometric solutions and the technical enforcement of this fundamental right. Faced with the mentioned vulnerabilities and requirements, the community has proposed primitives dedicated to biometrics, so called biometric template protection schemes (BTP), as well as privacy-preserving biometric recognition protocols. With the popularity of mobile smart devices, some security standards such as FIDO and BOPS have also emerged. The vast majority of these mechanisms and protocols, including the standards, are not systematically accompanied by formal security analyses. In this project, we will conduct a thorough analysis of existing solutions. By developing a better understanding of their security, we will define suitable security models and propose tailored primitives and protocols. The goal of the PRIVABIO project is to mitigate security and privacy problems in the use of biometrics, by offering low-cost protocols with security guarantees proportional to the high sensitivity of these personal data. PRIVABIO is divided into the following 4 axis: 1) Evaluate the security of biometric template protection (BTP) schemes and biometric protocols. 2) Provide more secure BTP schemes without degrading recognition accuracy, and without compromising their usability on resource-constrained devices. 3) Identify cryptographic mechanisms of interest for privacy aspects. 4) Integrate biometric and cryptographic mechanisms into multi-factor authentication protocols as well as into identification protocols, by providing formal security guarantees.

We will study the algorithmic complexity of metric-based covering problems in graphs, viewed as networks with their underlying distance-metric. Such problems have important applications, such as routing or monitoring in communication and transportation networks, information retrieval in graph databases, or computational learning in large datasets. They pose important technical challenges, as most classic techniques used for more localized graph problems fail in this context. Our objectives are, on one hand, to exhibit common properties of the inputs that render these problems intractable; on the other hand, to develop efficient algorithms for relevant classes. Our focus is the innovative use of structural graph parameters that are relevant for metric properties, like distance VC dimension, tree-length and hyperbolicity, and recently introduced parameters like MIM-width and twin-width. We will explore various settings, in particular, parameterized and enumeration algorithms.

In a context of an aging population and an increase in life expectancy related to the progress of medicine and a better prevention policy, the demand for geriatric care is likely to increase in the incoming years. This project aims to provide a decision support tool for better follow-up and efficient care of the elderly patient (EP) in the hospital from his/her first admission. The purpose of this tool is to rationalize all required resources to take care of EP by decompartmentalizing the services usually organized in silos and by favoring the collaboration of health professionals. The secondary objective is to propose a performance evaluation tool for the integrated care pathway of EP in order to quantify the impact of the proposed measures and prioritize the most efficient ones. This project is composed of 3 main phases. A first phase corresponding to the first scientific challenge relates to the modeling of the clinical pathways. A mixed approach to model the health status of EP combined with pathway modeling will be proposed. We are considering a similar approach in order to capture the complexity of the journey in the hospital and to emphasize the impact of the medical decisions on the health status of EP. In order to feed the model, a data collection will be carried out in collaboration with the University Hospital of Saint-Etienne and the Hospital of Roanne. Observations will be made in the facilities to understand clinical pathways, and hospital data will be used to build clinical pathways models and health status models. A second phase corresponds to the design of the hospital reorganization. For this, several implementation scenarios will be proposed via the optimization of the necessary resources. The model proposed in Phase 1 will be used to build a new service adapted to the population of interest while respecting the strong constraints of the hospital (constant resources, limited space). Several mixed-integer linear models based on a formalization of the problem using queueing theory will be proposed. Finally, the third phase aims at evaluating the performances of the organizational scenarios that will emerge from phase 2. A mixed simulation model with discrete / multi-agent events will be proposed with a view to a systemic approach. A joint assessment of flows and medico-economic aspects will be proposed through collaboration with a health economist. The objective is to simulate several possible scenarios to find the best management policy (discharge policy, diagnosis related group, ...) and the best sizing of services in the hospital in terms of quality of service / length of stay and cost (economic study). The first admission control of an EP is critical. It is crucial to propose a control strategy of the path of PA from its first admission to the hospital through a coordination of its journey in the hospital and after the stay, to avoid costly and disastrous rehospitalization for the health of the EP. Thus the approach proposed at the end of the project will provide healthcare professionals with methodological guides to better support the EP care, from his/her first visit, thus minimizing the risk of deterioration of health and increasing his/her life expectancy.

Hospitals and life-science institutes produce a tremendous amount of data on a daily basis during the healthcare process and ordinary scientific activity. Such data are highly valuable as they can be used to improve the process of care delivery and prevention and can also play a pivotal role in prospective clinical research. However, clinical, biological and imaging data are usually gathered by means of diverse data collection channels and procedures exhibiting a diverse degree of reliability and trustability. As a consequence, the collected data is usually scattered over heterogeneous data sources and suffers from quality problems that hampers its use for analysis purposes. Classical data quality issues can be observed, including missing or erroneous data, and also more complex problems can be perceived, for example due to secondary use in different contexts than the ones they were meant to be collected for. Additionally, the distribution of data can evolve over time creating “data-glitches” than can cause interpretation errors of high severity. Today, no system is able to assist the clinicians and researchers in a quality-aware exploration of their data. Overall, the lack of quality indicators strongly limits an in-depth use of healthcare data in translational research. We argue that more analyses of increasing complexity and more interactions between clinical and pre-clinical medical research would be feasible if the available data were annotated with quality indicators, and if such quality indicators were also employed in the querying and analysis of the available data. This research proposal is geared toward a system capable of capturing and formalizing the knowledge of data quality from domain experts, enriching the available data with this knowledge and thus exploiting this knowledge in the subsequent quality-aware medical research studies. We expect a quality-certified collection of medical and biological datasets, on which quality-certified analytical queries can be formulated. We envision the conception and implementation of a quality-aware query engine with query enrichment and answering capabilities. To reach this ambitious objectives, the following concrete scientific goals must be fulfilled : An innovative research approach, that starts from concrete datasets and expert practices and knowledge to reach formal models and theoretical solutions, will be employed to elicit innovative quality dimensions and to identify, formalize, verify and finally construct quality indicators able to capture the variety and complexity of medical data; those indicators have to be composed, normalized and aggregated when queries involve data with different granularities (e.g., accuracy indications on pieces of information at the patient level have to be composed when one queries cohort) and of different quality dimensions (e.g., mixing incomplete and inaccurate data); In turn, those complex aggregated indicators have to be used to provide new quality-driven query answering, refinement, enrichment and data analytics techniques. A key novelty of this project is the handling of data which are not rectified on the original database but sanitized in a query-driven fashion: queries will be modified, rewritten and extended to integrate quality parameters in a flexible and automatic way. The adequacy of our declarative specification of quality indicators, and the efficiency of query refinement and query answering, along with analytical tasks leveraging such indicators will be assessed by domain experts on real representative datasets collected by the project consortium.