Differential Privacy for Binary Functions via Randomized Graph Colorings

TL;DR

This paper introduces a graph-based framework for designing optimal differentially private mechanisms for binary functions, leveraging graph colorings and boundary properties to achieve minimal privacy loss.

Contribution

It provides a novel graph-theoretic approach to construct and analyze differentially private mechanisms for binary functions, including explicit formulas for optimal mechanisms.

Findings

Optimal mechanisms are uniquely determined by boundary conditions.

Closed-form expressions for optimal mechanisms on line graphs.

Balanced mechanisms depend only on distance to boundary, epsilon, and delta.

Abstract

We present a framework for designing differentially private (DP) mechanisms for binary functions via a graph representation of datasets. Datasets are nodes in the graph and any two neighboring datasets are connected by an edge. The true binary function we want to approximate assigns a value (or true color) to a dataset. Randomized DP mechanisms are then equivalent to randomized colorings of the graph. A key notion we use is that of the boundary of the graph. Any two neighboring datasets assigned a different true color belong to the boundary. Under this framework, we show that fixing the mechanism behavior at the boundary induces a unique optimal mechanism. Moreover, if the mechanism is to have a homogeneous behavior at the boundary, we present a closed expression for the optimal mechanism, which is obtained by means of a \emph{pullback} operation on the optimal mechanism of a line graph. For balanced mechanisms, not favoring one binary value over another, the optimal $(\epsilon,\delta)$-DP mechanism takes a particularly simple form, depending only on the minimum distance to the boundary, on $\epsilon$, and on $\delta$.