Rainbow Differential Privacy

TL;DR

This paper generalizes a framework for designing differentially private mechanisms from binary to multi-valued functions, introducing rainbow partitions and providing closed-form solutions for ternary functions.

Contribution

It extends the graph coloring approach for DP mechanisms to multi-valued functions and derives closed-form solutions for ternary cases.

Findings

Unique optimal DP mechanisms can be characterized by boundary conditions.

The framework can be extended to higher-dimensional query spaces.

Closed-form expressions are provided for ternary functions.

Abstract

We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions. As before, datasets are nodes in the graph and any two neighboring datasets are connected by an edge. In our setting, we assume that each dataset has a preferential ordering for the possible outputs of the mechanism, each of which we refer to as a rainbow. Different rainbows partition the graph of datasets into different regions. We show that if the DP mechanism is pre-specified at the boundary of such regions and behaves identically for all same-rainbow boundary datasets, at most one optimal such mechanism can exist and the problem can be solved by means of a morphism to a line graph. We then show closed form expressions for the line graph in the case of ternary functions. Treatment of ternary queries in this paper displays enough richness to be extended to higher-dimensional query spaces with preferential query ordering, but the optimality proof does not seem to follow directly from the ternary proof.