Situation-based privacy autonomous management for mobile social networks

As On-line Social Networks are nowadays largely used by mobile users and their posts potentially reveal - either explicitly or implicitly - much sensitive information about users, privacy control becomes a fundamental issue in such Mobile Social Networks (MSNs). In this paper we advocate that situational computing is the key ingredient for the development of effective mechanisms for privacy control in MSNs. We first describe an on-line survey carried out in order to understand the user's requirements regarding privacy when using MSNs. The results suggest that users have dynamic and context-dependent privacy requirements and also pinpoints which types of context data are more relevant for the decision about the user's willingness to share MSN content. Based on these findings, we propose SelPri, a solution developed as a proof of concept in form of an Android mobile social application that is integrated with Facebook. SelPri employs Fuzzy Logic to autonomously and dynamically adapt privacy settings of posts in MSNs according to the user's current situation, freeing the user from the hassle of the manual configuration of the privacy settings whenever his/her situation changes. We also describe conducted evaluations of the user experience in using SelPri to assess its accuracy to identify user situations, and its usability and effectiveness in meeting the user's dynamic and contextual privacy requirements.