Items shared through Social Media may affect more than one user's privacy—e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users’ privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users’ would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users’ behaviour.
@cite_27: 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.
In @cite_27 users must manually define for each item: the privacy settings for the item, their trust to the other users, the sensitivity of the item, and how much privacy risk they would like to take. These parameters are used to calculate what the authors call privacy risk and sharing loss on segments --- they define segments as the set of conflicting target users among a set of negotiating users. Then, based on these measures all of the conflicting target users in each segment are assigned the same action. That is, all of the conflicts that a set of negotiating users have would be solved either by granting or denying access. Clearly, not considering that each individual conflict can have a different solution leads to outcomes that are far from what the users would be willing to accept. Moreover, due to how the privacy risk and sharing loss metrics are defined, solutions are likely to be the actions preferred by the majority of negotiating users, which can be many times far from the actual behaviour of users as shown in Section .