User Motivation Based Privacy Preservation in Location Based Social Networks

Location based Social Networks (LBSNs) have become an integral part of mobile social networks, and with increasing popularity, the use of these networks has become more frequent. With the increasing use of these platforms, a lot of information is leaked, posing serious privacy threats to the users. To handle this, most platforms currently have different privacy settings that are extreme and render the processed checkin data useless to the user as the changes made completely deviates from the user motivation behind the check-in. To this end, we propose a model called User Motivation based Privacy Preservation (UMPP), which provides different privacy policies for different user motivations to retain user motivation for a check-in, which is otherwise lost in most other privacy policies in applications today. To the best of our knowledge, this is the first work that proposes user motivation based privacy policies. We evaluate the performance of our proposed methods on real- world datasets in terms of privacy and information loss.