Emergence of privacy conventions in online social networks

Abstract

Online social networks (OSNs) user data have a great commercial value to marketing companies, competing networking sites and identity thieves. With the emergence of new web technologies public developers are able to interface and extend the online websites services as applications. Proposing a fine-grained access control model for controlling application access to the OSN user data does not solve the problem of extension vulnerabilities. Because users might deny all the permissions or deny a subset of the permissions that might render the app non-usable. Moreover, it would be difficult for the app developers to design apps based on these diverse policy preferences. Therefore, it is important for the users to reach a consensus about the privacy settings of the apps they are interested about. My research goal is to design an agent-based approach that harnesses game theory and the dynamical properties of social network to facilitate agent reasoning for achieving optimal privacy conventions in the OSN and to develop decentralized learning mechanisms that facilitate controlled and fast convergence to optimal conventions.