Privacy-Preserving Data Publishing
In his premonitory book “The Assault on Privacy: Computers, Data Banks, and Dossiers,” Arthur R. Miller warns of the threat of information technology. 45 years later, we are all too aware of the importance of collecting, sharing, and analyzing sensitive data with care. In particular, the problem of sharing (or publishing) datasets is a critical one. Past attempts at data sharing through anonymization, though it sounds like a simple bullet-proof procedure, have been met with catastrophes. Incidents experienced by companies like AOL and Netflix remind us of the consequences of underestimating this problem.
The goal of this research is to design data sharing protocols and mechanisms that achieve theoretically-sound privacy guarantees such as differential privacy. This project seeks to explore solutions spanning multiple applications, ranging from secure data aggregation to micro-data publishing, as well as multiple technical approaches, ranging from secure multiparty computation to data synthesis.
- Plausible Deniability for Privacy-Preserving Data Synthesis
- Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks