Collaborative Privacy Management For Data Co-Ownership In Social Networks

Social networks have sprung up and become a hot issue of current society. In spite of the fact that these networks provide users with a variety of attractive features, much to users' dismay, however, they are likely to expose uses' private information.To address the privacy problem incurred by data co-ownership, we propose an approach which adopts collaborative management to obtain a joint decision reflecting the "general will". What's more, the Condorcet Voting Scheme is utilized to implement the collaborative management. Due to the fact that trust relations is inherent between people, therefore, our proposed algorithm utilizes trust relations in social networks and combines it with the Condorcet preferential voting scheme. It turns out that trust can exert an effective impact on the final results. The addition of trust can more conform to users'expectation, however, the time consuming is still hindering its promotion and application in practice, thus an optimization is further developed to improve its efficiency. Considering users might feel boredom in the case of large number of sharing resources, an inference technique is introduced to relieve user from the burden of massive manually input based on the user's voting history.In the second part of this thesis, we conduct a full-fledged privacy exploration against six typical social networking sites. Our study mainly revolves around the life cycle of users' private data, which pertains to collection, procession, retention and deletion. An exhaustive privacy analysis has been undertaken to reveal a wide range of privacy risks.