Obtaining $(\epsilon,\delta)$-differential privacy guarantees when using a Poisson mechanism to synthesize contingency tables

TL;DR

This paper presents a method to achieve $(,)$-differential privacy guarantees using a Poisson mechanism for synthesizing contingency tables, validated through empirical experiments on confidential databases.

Contribution

It introduces a novel approach to obtain $(,)$-differential privacy guarantees via the Poisson distribution's CDF in data synthesis.

Findings

Successfully achieved $(,)$-differential privacy guarantees.

Empirical validation on confidential database synthesis.

Demonstrated practical applicability of the Poisson mechanism.

Abstract

We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts in contingency tables. Specifically, we show how to obtain $(\epsilon, \delta)$-probabilistic differential privacy guarantees via the Poisson distribution's cumulative distribution function. We demonstrate this empirically with the synthesis of an administrative-type confidential database.