Learning-based privacy-preserving location sharing

Abstract

With the improvement of mobile communication technology, mobile Online Social Networks (mOSNs) provide users with the corresponding location based services when compared with traditional social networks. Location sharing becomes a fundamental component of mOSNs now, and some practical methods and techniques have been proposed to protect user’s privacy information. Some of these methods can accommodate privacy protection based on the input user profile and user’s privacy preferences through personalization, but user may be unlikely to use them without easy operation and strong privacy guarantee. In this article, we make a further research on privacy-preserving location sharing in mOSNs and develop a framework to help user to choose his desired degree of the privacy protection based on context aware. An adaptive learning model is established to provide user privacy right decisions, based on analyzing a series of factors that influence the choice of user’s privacy profile. This model will manage the different contexts of different user privacy preference with minimal user intervention and can achieve self-perfection gradually. So our proposed model can effectively protect users’ privacy and motivate users to make use of privacy preferences available to them.