With the rapid development of information technology, data availability is improved greatly. Data may be accessed at anytime by people from any location. However,threats to data security and privacy arise as one of the major problems of the development of information systems, especially those information systems which contain personal information. An association database is a personal information system which contains associations between persons. In this thesis, we identify the security and privacy problems of association databases. In order to solve these problems, we propose a new security and privacy model for association databases equipped with both direct access control and inference control mechanisms. In this model, there are multiple criteria including, not only confidentiality, but also privacy and other aspects of security to classify the association. The methods used in the system are: The direct access control method is based on the mandatory model; The inference control method is based on both logic reasoning and probabilistic reasoning (Belief Networks). My contributions to security and privacy model for association databases and to inference control in the model include: Identification of security and privacy problems in association databases; Formal definition of association database model; Representation association databases as directed multiple graphs; Development of axioms for direct access control; Specification of the unauthorized inference problem; A method for unauthorized inference detection and control that includes: Development of logic inference rules and probabilistic inference rule; Application of belief networks as a tool for unauthorized inference detection and control.

With the rapid development of information technology, data availability is improved greatly. Data may be accessed at anytime by people from any location. However,threats to data security and privacy arise as one of the major problems of the development of information systems, especially those information systems which contain personal information. An association database is a personal information system which contains associations between persons. In this thesis, we identify the security and privacy problems of association databases. In order to solve these problems, we propose a new security and privacy model for association databases equipped with both direct access control and inference control mechanisms. In this model, there are multiple criteria including, not only confidentiality, but also privacy and other aspects of security to classify the association. The methods used in the system are: The direct access control method is based on the mandatory model; The inference control method is based on both logic reasoning and probabilistic reasoning (Belief Networks). My contributions to security and privacy model for association databases and to inference control in the model include: Identification of security and privacy problems in association databases; Formal definition of association database model; Representation association databases as directed multiple graphs; Development of axioms for direct access control; Specification of the unauthorized inference problem; A method for unauthorized inference detection and control that includes: Development of logic inference rules and probabilistic inference rule; Application of belief networks as a tool for unauthorized inference detection and control.

Dans cette thèse, nous proposons une méthode pour construire un outil d'aide à la décision pour le diagnostic de maladie rare. Nous cherchons à minimiser le nombre de tests médicaux nécessaires pour atteindre un état où l'incertitude concernant la maladie du patient est inférieure à un seuil prédéterminé. Ce faisant, nous tenons compte de la nécessité dans de nombreuses applications médicales, d'éviter autant que possible, tout diagnostic erroné. Pour résoudre cette tâche d'optimisation, nous étudions plusieurs algorithmes d'apprentissage par renforcement et les rendons opérationnels pour notre problème de très grande dimension. Pour cela nous décomposons le problème initial sous la forme de plusieurs sous-problèmes et montrons qu'il est possible de tirer partie des intersections entre ces sous-tâches pour accélérer l'apprentissage. Les stratégies apprises se révèlent bien plus performantes que des stratégies gloutonnes classiques. Nous présentons également une façon de combiner les connaissances d'experts, exprimées sous forme de probabilités conditionnelles, avec des données cliniques. Il s'agit d'un aspect crucial car la rareté des données pour les maladies rares empêche toute approche basée uniquement sur des données cliniques. Nous montrons, tant théoriquement qu'empiriquement, que l'estimateur que nous proposons est toujours plus performant que le meilleur des deux modèles (expert ou données) à une constante près. Enfin nous montrons qu'il est possible d'intégrer efficacement des raisonnements tenant compte du niveau de granularité des symptômes renseignés tout en restant dans le cadre probabiliste développé tout au long de ce travail. / In this thesis, we propose a method to build a decision support tool for the diagnosis of rare diseases. We aim to minimize the number of medical tests necessary to achieve a state where the uncertainty regarding the patient's disease is less than a predetermined threshold. In doing so, we take into account the need in many medical applications, to avoid as much as possible, any misdiagnosis. To solve this optimization task, we investigate several reinforcement learning algorithm and make them operable in our high-dimensional. To do this, we break down the initial problem into several sub-problems and show that it is possible to take advantage of the intersections between these sub-tasks to accelerate the learning phase. The strategies learned are much more effective than classic greedy strategies. We also present a way to combine expert knowledge, expressed as conditional probabilities, with clinical data. This is crucial because the scarcity of data in the field of rare diseases prevents any approach based solely on clinical data. We show, both empirically and theoretically, that our proposed estimator is always more efficient than the best of the two models (expert or data) within a constant. Finally, we show that it is possible to effectively integrate reasoning taking into account the level of granularity of the symptoms reported while remaining within the probabilistic framework developed throughout this work.