PANO: Privacy Auctioning for Online Social Networks

Abstract

Online Social Networks (OSNs) enable their users to share content with their connections. Shared contents over OSNs raise privacy concerns, since they tend to contain personal information of users. More importantly, a single content, e.g, a photo of a group of people, can potentially contain private information of multiple users, which become available without their consent. Ensuring that all relevant users' privacy requirements are met is important but difficult since the requirements can easily be conflicting. Hence, mechanisms to resolve privacy disputes are needed. Accordingly, this paper proposes an agent-based collaborative privacy management model, where agents represent users and manage their privacy requirements. When an image is about to be shared, the relevant agents enter an auction and bid on behalf of their users about how private the considered image is. The bids are processed with a modified version of Clarke-Tax mechanism that achieves fair handling of privacy settings and taxes the agents whose privacy settings are chosen.