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.
@cite_40: 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.
The work described in @cite_40 is based on an incentive mechanism where users are rewarded with a quantity of numeraire each time they share information or acknowledge the presence of other users (called co-owners) who are affected by the same item. When there are conflicts among co-owners' policies, users can spend their numeraire bidding for the policy that is best for them. Then, the use of the Clark Tax mechanism is suggested to obtain the highest bid. As stated in @cite_27 , users may have difficulties to comprehend the mechanism and specify appropriate bid values in auctions. Furthermore, users that earned much numeraire in the past will have more numeraire to spend it at will, potentially leading to unilateral decisions.