Differential Privacy: Recent Developments and Future Challenges
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.