Social media systems have transformed our societal communications, including news discovery, recommendations, societal interactions, E-commerce, as well as political and governance activities. However, the rising popularity of social media systems has brought concerns about security and privacy to the forefront. This project aims to design trustworthy social systems by building on the discipline of network science. First, the project is developing techniques for analysis of social media data that protect against risks to individual privacy; new research is needed since existing approaches are unable to provide rigorous privacy guarantees. Second, the project is developing new approaches to mitigate the threat of "fake accounts" in social systems, in spite of attempts by the creators of those accounts to elude detection. Both deployed and academic approaches remain vulnerable to strategic adversaries, motivating the development of novel defense mechanisms based on network science. The findings and new designs from this research will directly impact the security and privacy of a broad class of social network users. The private network analytics thrust builds on the ideas of differential privacy, ensuring sufficient uncertainty in results to hide individual relationships. The project introduces dependent differential privacy, which protects against disclosure of information associated with an individual, as well as mutual information privacy, an entropy-based measure. The Sybil mitigation thrust is based on the idea of adversarial machine learning: the creators of fake accounts are presumed to adapt their mechanisms to changing detection approaches. This work exploits new features, such as temporal dynamics of the network, to address this problem. Finally, the project aims to integrate the research with an educational initiative for developing pedagogical approaches and content for trustworthy social systems.
CAREER: Trustworthy Social Systems Using Network Science