Anonymization: Enhancing Privacy and Security of Sensitive Data of Online Social Networks

نویسندگان

  • Mr. Gaurav
  • P. R. Mr. Gururaj
چکیده

Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. This paper is motivated by the recognition of the need for a finer grain and personalized privacy in data publication of social netwoks.Recently , researchers have proposed a privacy protection scheme that not only prevents the disclosure of identity of users but also the disclosure of selected features in user’s profiles. An individual user can select which features of his profiles he wishes to conceal. The social networks are modeled as graphs in which users are nodes and features are labels. Labels are denoted either as sensitive or as non-sensitive. We treat node labels both as background knowledge an adversary may possess, and as sensitive information that has to be protected. Privacy protection algorithms that allow for graph data to be published in a form such that an adversary who possesses information about a nodes neighborhood cannot safely infer its identity and its sensitive labels. To this aim, the algorithms transform the original graph into a graph in which nodes are sufficiently indistinguishable. The algorithms are designed to do so while losing as little information and while preserving as much utility as possible. We evaluate empirically the extent to which algorithms preserve the original graph’s structure and properties. We show that our solution is effective, efficient and scalable while offering stronger privacy guarantees than those in previous research. Privacy models evolved w.r.t kanonymity to prevent node reidentification through structure information.

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تاریخ انتشار 2014