Differential Privacy: Recent Developments and Future Challenges

Consider a database of sensitive information about a set of participants. Statistical analysis of the data may yield valuable results, but it also poses serious threats to participants' privacy. A successful research program has, in the last few years, attempted to address these conflicting concerns. This line of work formulated the rigorous privacy guarantee of differential privacy [Dwork McSherry Nissim and Smith '06] and showed that, in some cases, data analyses can provide accurate answers while protecting participants' privacy.

I will review some of this past work, and then introduce new general-purpose tools for privacy preserving data analysis: 1. A "multiplicative weights" framework for fast and accurate differentially private algorithms. 2. New privacy analysis techniques, including robust privacy guarantees for differential privacy under composition.

We will use these tools to show that differential privacy permits surprisingly rich and accurate data analyses. I will then highlight some of the intriguing challenges that remain open for future work in this field.

No prior knowledge will be assumed.

Guy Rothblum is a postdoctoral research fellow at Princeton University, supported by a Computing Innovation Fellowship. He completed his Ph.D. in computer science at MIT, advised by Shafi Goldwasser. His research interests are in theoretical computer science, especially privacy-preserving data analysis, cryptography and complexity theory.