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.
Until now, very few researchers considered the problem of resolving conflicts in multi-party privacy management for Social Media. @cite_5 proposed a method to define privacy policies collaboratively. In their approach all of the parties involved can define strong and weak privacy preferences. However, this approach does not involve any automated method to solve conflicts, only some suggestions that the users might want to consider when they try to solve the conflicts manually.
@cite_5
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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. 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. Existing online social networks (OSNs) only allow a single user to restrict access to her his data but cannot provide any mechanism to enforce privacy concerns over data associated with multiple users. This situation leaves privacy conflicts largely unresolved and leads to the potential disclosure of users' sensitive information. To address such an issue, a MultiParty Access Control (MPAC) model was recently proposed, including a systematic approach to identify and resolve privacy conflicts for collaborative data sharing in OSNs. In this paper, we take another step to further study the problem of analyzing the strategic behavior of rational controllers in multiparty access control, where each controller aims to maximize her his own benefit by adjusting her his privacy setting in collaborative data sharing in OSNs. We first formulate this problem as a multiparty control game and show the existence of unique Nash Equilibrium (NE) which is critical because at an NE, no controller has any incentive to change her his privacy setting. We then present algorithms to compute the NE and prove that the system can converge to the NE in only a few iterations. A numerical analysis is also provided for different scenarios that illustrate the interplay of controllers in the multiparty control game. In addition, we conduct user studies of the multiparty control game to explore the gap between game theoretic approaches and real human behaviors. The workload needed for managing privacy and publicness in current social network services (SNSs) is placed on individuals, yet people have few means to control what others disclose about them. This paper considers SNS-users' concerns in relation to online disclosure and the ways in which they cope with these both individually and collaboratively. While previous work has focused mainly on individual coping strategies, our findings from a qualitative study with 27 participants suggest that collaborative strategies in boundary regulation are of additional importance. We present a framework of strategies for boundary regulation that informs both theoretical work and design practice related to management of disclosure in SNSs. The framework considers disclosure as an interpersonal process of boundary regulation, in which people are dependent on what others choose to disclose about them. The paper concludes by proposing design solutions supportive of collaborative and preventive strategies in boundary regulation that facilitate the management of disclosure online.