Differential Privacy for Sets in Euclidean Space

TL;DR

This paper introduces a new differential privacy framework for set-valued data in Euclidean space, utilizing capacity functionals, and proposes a Laplacian Perturbation Mechanism to achieve privacy guarantees with practical numerical validation.

Contribution

It extends differential privacy to set-valued data using capacity functionals and introduces a Laplacian Perturbation Mechanism for privacy preservation.

Findings

The Laplacian Perturbation Mechanism achieves ?-differential privacy.

Theoretical analysis confirms privacy guarantees.

Numerical results demonstrate practical applicability.

Abstract

As multi-agent systems become more numerous and more data-driven, novel forms of privacy are needed in order to protect data types that are not accounted for by existing privacy frameworks. In this paper, we present a new form of privacy for set-valued data which extends the notion of differential privacy to sets which users want to protect. While differential privacy is typically defined in terms of probability distributions, we show that it is more natural here to define privacy for sets over their capacity functionals, which capture the probability of a random set intersecting some other set. In terms of sets' capacity functionals, we provide a novel definition of differential privacy for set-valued data. Based on this definition, we introduce the Laplacian Perturbation Mechanism (so named because it applies random perturbations to sets), and show that it provides ?-differential privacy as prescribed by our definition. These theoretical results are supported by numerical results, demonstrating the practical applicability of the developments made.