Using Participants' Utility Functions to Compare Versions of Differential Privacy

TL;DR

This paper applies decision theory and utility functions to compare different differential privacy variants, highlighting how individual preferences influence which privacy guarantees are most appealing.

Contribution

It introduces a utility-based framework for comparing differential privacy variants, emphasizing the role of individual preferences and decision settings.

Findings

Utility functions significantly affect privacy preference outcomes.

The choice of privacy parameters $psilon$ and $elta$ impacts individual preferences.

The Euclidean metric effectively measures changes in expected utilities.

Abstract

We use decision theory to compare variants of differential privacy from the perspective of prospective study participants. We posit the existence of a preference ordering on the set of potential consequences that study participants can incur, which enables the analysis of individual utility functions. Drawing upon the theory of measurement, we argue that changes in expected utilities should be measured via the classic Euclidean metric. We then consider the question of which privacy guarantees would be more appealing for individuals under different decision settings. Through our analysis, we found that the nature of the potential participant's utility function, along with the specific values of $\epsilon$ and $\delta$, can greatly alter which privacy guarantees are preferable.