Technical Case Study of Privacy-Enhancing Technologies (PETs) for Public Health

TL;DR

This paper presents a case study on using Differential Privacy to generate synthetic financial data that, combined with public health datasets, supports privacy-preserving epidemiological analysis and decision-making.

Contribution

It introduces a novel application of Differential Privacy to create synthetic data for public health, enabling secure data sharing and analysis.

Findings

Synthetic data retains spatial-temporal and predictive power.

Development of reusable tools for epidemiological analysis.

Effective privacy-preserving data sharing practices.

Abstract

We present a technical case study on the Privacy-Enhancing Technologies (PETs) for Public Health Challenge, a collaborative effort to safely leverage sensitive private sector data for social impact, specifically pandemic management. The project utilized Differential Privacy (DP) to create realistic, privacy-preserved synthetic financial transaction data, which was then combined with public health and mobility datasets. This approach successfully addressed the critical hurdle of sharing sensitive financial information for research and policy. The analysis demonstrated that this synthetic, DP-protected data possesses significant spatial-temporal and predictive power for public health. Key outcomes include the development of six reusable tools and frameworks supporting diagnostic nowcasting (e.g., Hotspot Detection, Pandemic Adherence Monitoring) and predictive forecasting (e.g., Mobility Analysis, Contact Matrix Estimation) for epidemiological decision-making. The study provides best practices for advancing data sharing in a privacy-compliant manner.