Maintain Data Integrity And Protection Of Private Label Information In Social Network Data

Abstract

Perceptive information about users of the social networks should be protected. The confront is to plan methods to publish social network data in a form that affords usefulness without compromising privacy. Previous research has proposed a variety of privacy models with the corresponding protection mechanisms that put off both unintentional private information escape and attacks by malicious adversaries. These early privacy models are mainly disturbed with identity and link revelation. The social networks are modelled as graphs in which users are nodes and social connections are edges. The intimidation definitions and defence mechanisms leverage structural properties of the graph. This paper is stimulated by the recognition of the need for a better grain and more personalized privacy. Users commend social networks such as Face book and LinkedIn with a wealth of personal information such as their age, address, current location or political orientation. We refer to these details and messages as features in the user's profile. We propose a privacy protection scheme that not only put off the revelation of identity of users but also the disclosure of selected features in users' profiles.