A Privacy Protection Model for Online Social Networks

Online Social Networks (OSNs) have become an important part of daily digital interactions for more than half a billion users around the world. Unconstrained by physical spaces, OSNs offer to web users new interesting means to communicate, interact, and socialize. While these networks make frequent data sharing and inter-user communications instantly possible, privacy-related issues are their obvious much discussed immediate consequences. Recent research identifies a growing privacy problem that exists within OSNs. Several studies have shown how easily strangers can extract personal data about users from the OSNs. There is need for automatic and easy to use privacy protection mechanism. We propose social interaction based audience segregation model for online social networks. Our model uses type, frequency, and initiation factor of social interactions to calculate relationship strength. This model mimics real life interaction patterns and makes online social networks more privacy friendly.