Social media involve many shared items, such as photos, which may concern more than one user. The challenge is that users’ individual privacy preferences for the same item may conflict, so an approach that simply merges in some way the users’ privacy preferences may provide unsatisfactory results. Previous proposals to deal with the problem were either time-consuming or did not consider compromises to solve these conflicts (e.g., by considering unilaterally imposed approaches only). We propose a negotiation mechanism for users to agree on a compromise for these conflicts. The second challenge we address in this article relates to the exponential complexity of such a negotiation mechanism. To address this, we propose heuristics that reduce the complexity of the negotiation mechanism and show how substantial benefits can be derived from the use of these heuristics through extensive experimental evaluation that compares the performance of the negotiation mechanism with and without these heuristics. Moreover, we show that one such heuristic makes the negotiation mechanism produce results fast enough to be used in actual social media infrastructures with near-optimal results.
In @cite_28 , users must manually define their to other users, the sensitivity that each of the items has for them, and their general privacy concern. Then, the authors use these parameters to calculate two main measures, privacy risk and sharing loss. In particular, they calculate the privacy risk and the sharing loss on what they call segments --- in our terminology, a segment equals the set of agents in conflict --- as a whole, i.e. all of the agents in these segments are assigned the action preferred by either one party or the other in the negotiation. That is, in our terminology only two action vectors --- @math and @math induced by the privacy policies @math and @math respectively --- are considered, and the action vector chosen is the one that maximises the tradeoff between privacy risk and sharing loss. Clearly, not considering other possible action vectors could lead to outcomes that are far from optimal.
@cite_28
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We have seen tremendous growth in online social networks (OSNs) in recent years. These OSNs not only offer attractive means for virtual social interactions and information sharing, but also raise a number of security and privacy issues. Although OSNs allow a single user to govern access to her his data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users, remaining privacy violations largely unresolved and leading to the potential disclosure of information that at least one user intended to keep private. In this paper, we propose an approach to enable collaborative privacy management of shared data in OSNs. In particular, we provide a systematic mechanism to identify and resolve privacy conflicts for collaborative data sharing. Our conflict resolution indicates a tradeoff between privacy protection and data sharing by quantifying privacy risk and sharing loss. We also discuss a proof-of-concept prototype implementation of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology.