Privacy loss in Online Social Network

Abstract

In the digital age, we are intricately connected through various social networks. Social networking platforms serve as avenues for communication, allowing individuals to interact with others through their profiles on specific networking platforms. Enormous amounts of data are generated every minute, driven by the vast user base of these platforms. Internet of Things (IoT)-based social networking platforms enable individuals to share public or private information, though users are cautious due to the critical importance of certain data. Despite the potential for information sharing in today's global internet era, social networks pose a serious threat to user privacy. Users may hesitate to share certain information, as the involvement of multiple users in sharing a data item puts the privacy of individuals at risk. While restrictions exist for users seeking access to others' data, these restrictions may not apply to posts, which are integral components of the social networking experience. In current online social networks, restrictions do not extend to the sharing of co-owned data. Each user holds their own opinion on who can access their data, and the posting of data is influenced by the opinions of the associated users involved. This thesis report explores how privacy loss is calculated for users involved in a group, considering different values of sensitivity and reputation. The computation of reputation is crucial in determining privacy loss, offering insights into the dynamics of information sharing within social networks.