Privacy managment in online social networks

Abstract

People are willing to share their personal information in social networks. The users are allowed to create and share content about themselves and others. When multiple entities start distributing content without a control, information can reach unintended individuals and inference can reveal more information about the user. This thesis rst categorizes the privacy violations that take place in online social networks. Our proposed approach is based on agent-based representation of a social network, where the agents manage users' privacy requirements by creating commitments with the system. The privacy context, including the relations among users or content types are captured using description logic. We propose a sound and complete algorithm to detect privacy violations on varying depths of social networks. We implement the proposed model and evaluate our approach using real-life social networks. A content that is shared by one user can very well violate the privacy of other users. To remedy this, ideally, all the users that are related to a content should get a say in how the content should be shared. To enable this, we model users of the social networks as agents that represent their users' privacy constraints as semantic rules. In one line, we propose a reciprocity-based negotiation for reaching privacy agreements among users and introduce a negotiation architecture that combines semantic privacy rules with utility functions. In a second line, we propose a privacy framework where agents use Assumption-based Argumentation to discuss with each other on propositions that enable their privacy rules by generating facts and assumptions from their ontology.