Functional Approximation Methods for Differentially Private Distribution Estimation

TL;DR

This paper presents new functional approximation techniques for differentially private CDF estimation, enabling efficient and accurate privacy-preserving distribution analysis suitable for streaming and decentralized data.

Contribution

It introduces polynomial projection and sparse approximation methods for private CDF estimation, advancing practical approaches with systematic evaluation of function spaces.

Findings

Methods achieve comparable or better accuracy than existing approaches.

Effective in decentralized and streaming data scenarios.

Systematic analysis of dictionary choices impacts performance.

Abstract

The cumulative distribution function (CDF) is fundamental for characterizing random variables, making it essential in applications that require privacy-preserving data analysis. This paper introduces a novel framework for constructing differentially private CDFs inspired by functional analysis and the functional mechanism. We develop two variants: a polynomial projection method, which projects the empirical CDF into a polynomial space, and a sparse approximation method via matching pursuit, which projects it into arbitrary function spaces constructed from dictionaries. In both cases, the empirical CDF is approximated within the chosen space, and the corresponding coefficients are privatized to guarantee differential privacy. Compared with existing approaches such as histogram queries and adaptive quantiles, our methods achieve comparable or superior performance. Our methods are particularly well-suited to decentralized settings and scenarios where CDFs must be efficiently updated with newly collected or streaming data. In addition, we investigate the influence of parameters such as dictionary size and systematically evaluate different dictionary constructions, including Legendre polynomials, B-splines, and distribution-based functions. Overall, our contributions advance the development of practical and reliable methods for privacy-preserving CDF estimation.