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InfoResearchPeer-reviewed

Functional Approximation Methods for Differentially Private Distribution Estimation

Source
IEEE Xplore (Security & AI Journals)(opens in a new tab)
Published
Record updated

Summary

This paper presents a framework for building differentially private cumulative distribution functions (CDFs) based on functional analysis and the functional mechanism. It introduces two variants, a polynomial projection method and a sparse approximation method via matching pursuit, which approximate the empirical CDF and privatize the resulting coefficients. The authors report performance comparable or superior to histogram queries, tree-based methods, and adaptive quantiles.