Privacy Management in Agent-Based Social Networks: (Doctoral Consortium)

In online social networks (OSNs), users are allowed to create and share content about themselves and others. When multiple entities start distributing content, information can reach unintended individuals and inference can reveal more information about the user. Existing applications do not focus on detecting privacy violations before they occur in the system. This thesis proposes an agent-based representation of a social network, where the agents manage users’ privacy requirements and create privacy agreements with agents. The privacy context, such as the relations among users, various content types in the system, and so on are represented with a formal language. By reasoning with this formal language, an agent checks the current state of the system to resolve privacy violations before they occur. We argue that commonsense reasoning could be useful to solve some of privacy examples reported in the literature. We will develop new methods to automatically identify private information using commonsense reasoning, which has never been applied to privacy context. Moreover, agents may have conflicting privacy requirements. We will study how to use agreement technologies in privacy settings for agents to resolve conflicts automatically.