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Detecting and preventing inference attacks in online social networks: A data-driven and holistic framework

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  • Xiaoyun He
  • Haibing Lu

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.

Suggested Citation

  • Xiaoyun He & Haibing Lu, 2017. "Detecting and preventing inference attacks in online social networks: A data-driven and holistic framework," Journal of Information Privacy and Security, Taylor & Francis Journals, vol. 13(3), pages 104-119, July.
    • Handle: RePEc:taf:uipsxx:v:13:y:2017:i:3:p:104-119
    DOI: 10.1080/15536548.2017.1357383
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