Towards Practicing Privacy in Social Networks

Abstract

Information privacy is vital for establishing public trust on the Internet. However, as online social networks (OSNs) step into literally every aspect of our life, they also further erode our personal privacy to an unprecedented extent. Today, network data releasing and inadvertent OSN privacy settings have become two main channels causing such privacy leakage. As such, this thesis studies the challenges for privacy preservation raised in the above two settings. For the first setting, we utilize the hierarchical random graph model to develop two privacy preserving techniques. The two approaches ground privacy from two disparate perspectives, one from anonymity and another from differential privacy. Experiments show both methods can preserve network utility well while preserving privacy. For the second setting, we propose to integrate peer effects into collaborative access control strategies to mitigate conflicts among OSN users? different privacy preferences. We also develop a Facebook app to materialize our solution.