Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released…

• NS
  National Science Foundation
• CI
  California Institute of Technology
• DF
  Directorate for Computer and Information Science and Engineering

  Award: 1252292