Social Interaction Based Audience Segregation for Online Social Networks.

Online social networking is the latest craze that has captured the attention of masses, people use these sites to communicate with their friends and family. These sites o er attractive means of social interac- tions and communications, but also raise privacy concerns. This paper examines user's abilities to control access to their personal information posted in online social networks. Online social networks lack common mechanism used by individuals in their real life to manage their privacy. The lack of such mechanism signi cantly a ects the level of user control over their self presentation in online social networks. In this paper, we present social interaction based audience segregation model for online so- cial networks. This model mimics real life interaction patterns and makes online social networks more privacy friendly. Our model uses type, fre- quency, and initiation factor of social interactions to calculate friendship strength. The main contribution of the model is that it considers set of all possible interactions among friends and assigns a numerical weight to each type of interaction in order to increase or decrease its contribu- tion in calculation of friendship strength based on its importance in the development of relationship ties

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project