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

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Provided by: International Journal of Computer Science and Information Technologies
Topic: Security
Format: PDF
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 networks. 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.
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