IPAM Workshop: Statistical and Learning-Theoretic Challenges in Data Privacy

Location: Los Angeles, California

The goal of workshop is to establish a coherent theoretical foundation for research on data privacy. This implies work on (1) how the conflicting goals of privacy and utility can or should be formulated mathematically; and (2) how the constraints of privacy—in their various incarnations—affect the accuracy of statistical inference and machine learning. In particular, the goal is to shed light on the interplay between privacy and concepts such as consistency and efficiency of estimators, generalization error of learning, robustness and stability of estimation algorithms, and the generation of synthetic data.

More information here and here.