Prior-Aware Distribution Estimation for Differential Privacy

TL;DR

This paper introduces a method for estimating data distributions under differential privacy by leveraging prior information and private answers, aiming to improve accuracy while minimizing divergence from a prior distribution.

Contribution

It presents an iterative optimization approach that combines private answers and prior data to estimate distributions, advancing differential privacy techniques.

Findings

Achieved second place in NIST 2020 Differential Privacy Temporal Map Challenge.

Developed an effective method for distribution estimation using private answers and prior data.

Demonstrated improved accuracy in workload estimation under differential privacy constraints.

Abstract

Joint distribution estimation of a dataset under differential privacy is a fundamental problem for many privacy-focused applications, such as query answering, machine learning tasks and synthetic data generation. In this work, we examine the joint distribution estimation problem given two data points: 1) differentially private answers of a workload computed over private data and 2) a prior empirical distribution from a public dataset. Our goal is to find a new distribution such that estimating the workload using this distribution is as accurate as the differentially private answer, and the relative entropy, or KL divergence, of this distribution is minimized with respect to the prior distribution. We propose an approach based on iterative optimization for solving this problem. An application of our solution won second place in the NIST 2020 Differential Privacy Temporal Map Challenge, Sprint 2.