Special Issue Article Open Access

Preventing Private Information Leakage on Social mining

Abstract

Online social networks, such as facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. Hence managing those confidential information leakage is an challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since our goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques are explored and use methods of collective inference to discover sensitive attributes of the user profile data set. Hence sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.Keywords: social networking, social network privacy, sanitization, collective inference

A.Krishna Kumar , M.Suriya

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