Functional Approximation Methods for Differentially Private Distribution Estimation

This research paper presents new methods for creating differentially private CDFs (cumulative distribution functions, which describe how data is distributed), using techniques like polynomial projection and sparse approximation. The approach protects individual data privacy while still allowing accurate statistical analysis, and works well with streaming data and multiple variables.