Detecting and preventing inference attacks in online social networks: A data-driven and holistic framework. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: He, Xiaoyun; Lu, Haibing
• Is Part Of: Journal of information privacy & security. Volume 13:Issue 3(2017)
• Page Start: 104
• Page End: 119
• Publication Date: 2017-07-03
• Subjects: Privacy, Right of -- Periodicals; Data protection -- Periodicals; Computer security -- Periodicals; Computer security; Data protection; Privacy, Right of; Periodicals; 005.8
• DOI: 10.1080/15536548.2017.1357383
• Languages: English
• ISSNs: 1553-6548

Abstract

ABSTRACT: With increasing user involvement, social networks nowadays serve as a repository of all kinds of information. While there have been various studies demonstrating that private information can be inferred from social networks, few have taken a holistic view on designing mechanisms to detect and alleviate the inference attacks. In this study, we present a framework that leverages the social network data and data mining techniques to proactively detect and prevent possible inference attacks against users. A novel method is proposed to minimize the modifications to user profiles in order to prevent inference attacks while preserving the utility.