Exploiting community detection to recommend privacy policies in decentralized online social networks

Abstract

The usage of Online Social Networks (OSNs) has become a daily activity for billions of people that share their contents and personal information with the other users. Regardless of the platform exploited to provide the OSNs’ services, these contents’ sharing could expose the OSNs’ users to a number of privacy risks if proper privacy-preserving mechanisms are not provided. Indeed, users must be able to define its own privacy policies that are exploited by the OSN to regulate access to the shared contents. To reduce such users’ privacy risks, we propose a Privacy Policies Recommended System (PPRS) that assists the users in defining their own privacy policies. Besides suggesting the most appropriate privacy policies to end users, the proposed system is able to exploits a certain set of properties (or attributes) of the users to define permissions on the shared contents. The evaluation results based on real OSN dataset show that our approach classifies users with a higher accuracy by recommending specific privacy policies for different communities of the users’ friends.