Abstrait

A Study of Privacy Attacks on Social Network Data

Sri Krishna Adusumalli, Valli Kumari Vatsavayi, Jyothi Vadisala

Online social networks have become an important component of the online activities on the network and one of the most influencing media. Unconstrained by physical spaces, online social networks extend to web users new interesting means to communicate, interact and socialize. While these networks get to frequent data sharing and inter-user communications instantly possible, privacy related issues are their obvious much discussed immediate consequences. Although the impression of privacy may take different forms, the ultimate challenge is how to prevent privacy invasion when much personal information is useable. In this context, we address privacy related issues by resorting to social network analysis. Most of the state-of-art methods focus on vertex re-identification or identity disclosure in a social network. However, in real world social network scenario, vertices are usually associated with sensitive data like disease in health networks. In this paper, we study the literature on privacy in social networks. We formally specify the possible privacy breaches and describe the privacy attacks that have been examined. We represent different categories of privacy disclosures, background knowledge in concert with existing privacy preserving techniques in social network data publishing. We identify a new challenge in sensitive attribute disclosure based on different background knowledge like vertex degree pair of edge and vertex degree.