The increasing popularity of large-scale data analysis raises privacy concerns. The tremendous amount of data collected by data curators such as search engines, social network platforms, and medical institutions contain potentially sensitive information about individuals. With the rapid emergence of data-driven technologies, it has been increasingly important to respect the privacy of individuals. A central question is: how to build privacy-preserving algorithms to protect individual privacy without sacrificing the utility in a large degree? This project aims to develop rigorous tools and methodologies to analyze privacy-preserving algorithms.
The research objective of this project is to develop statistical theories and applications of privacy-preserving algorithms. In particular, the technical goals include (1) the statistical optimality of privacy-preserving algorithms in parametric models; (2) the statistical optimality and adaptivity of privacy-preserving algorithms in nonparametric regression, with focus on random forests algorithms, and; (3) the stability of privacy-preserving algorithms with applications to post-selection inference and adversarial robustness of deep neural networks. The new theoretical understandings will not only shed light on current privacy-preserving methodologies but also lead to new methodological developments of stable and adversarially robust algorithms.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.