Random Differential Privacy

TL;DR

This paper introduces random differential privacy (RDP), a relaxed privacy concept where only randomly drawn new data points must have limited influence on outputs, leading to more accurate data releases like histograms.

Contribution

The paper proposes the RDP framework, extending differential privacy with a focus on random data additions, and demonstrates its advantages in accuracy and composition properties.

Findings

RDP histograms are significantly more accurate than traditional DP histograms.

An analog of the composition property applies to RDP.

The paper develops a sensitivity framework for RDP-based function releases.

Abstract

We propose a relaxed privacy definition called {\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure. Random differential privacy requires that adding a {\em randomly drawn new observation} to a database will have small effect on the output. We show an analog of the composition property of differentially private procedures which applies to our new definition. We show how to release an RDP histogram and we show that RDP histograms are much more accurate than histograms obtained using ordinary differential privacy. We finally show an analog of the global sensitivity framework for the release of functions under our privacy definition.