document: Collective privacy management in social networks Social Networking is one of the major technological phenomena of the Web 2.0 , with hundreds of millions of people participating . Social networks enable a form of self expression for users , and help them to socialize and share content with other users . In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites , Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content . In this paper , we model the problem of collaborative enforcement of privacy policies on shared data by using game theory . In particular , we propose a solution that offers automated ways to share images based on an extended notion of content ownership . Building upon the Clarke-Tax mechanism , we describe a simple mechanism that promotes truthfulness , and that rewards users who promote co-ownership . We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture . To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed . In the paper , we also show a proof-of-concept application , which we implemented in the context of Facebook , one of today 's most popular social networks . We show that supporting these type of solutions is not also feasible , but can be implemented through a minimal increase in overhead to end-users .
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