Agents utilisateurs pour la protection des données personnelles : modélisation logique et outils informatiques. (User agents for personal data protection: logical modelling and computing tools)

Usage in the domain of multi-agent systems has evolved so as to integrate human users more closely in the applications. Manipulation of private information by autonomous agents has then called for an adapted protection of personal data. This work first examines the legal context of privacy protection and the various computing methods aiming at personal data protection. Surveys show a significant need for AI-based solutions, allowing both reasoning on the regulations themselves and automatically adapting an agent’s behaviour to these regulations. The PrivacyAware (PAw) agent model and the Deontic Logic for Privacy, designed to deal with regulations coming from multiple authorities, are proposed here in this perspective. The agent’s normative reasoning component analyses its heterogeneous context and provides a consistent policy for dealing with personal information. PAw agent then automatically controls its own usage of the data with regard to the resulting policy. In order to enforce policies in a remote manner, we study the different distributed application architectures oriented towards privacy protection, several of them based on the principles of Trusted Computing. We propose a complementary one, illustrating a different possible usage of this technology. Implementation of the PAw agent allows demonstration of its principles over three scenarios, thus showing the adaptability of the agent to its normative context and the influence of the regulations over the behaviour of the application.