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

2026-03-13

AI Summary

This study addresses the critical challenge of securely leveraging sensitive private-sector data—such as financial transactions—for public health decision-making, particularly in pandemic response, while preserving individual privacy. It introduces, for the first time, a systematic application of differentially private synthetic data generation to public health, producing high-fidelity synthetic financial transaction records that retain spatiotemporal characteristics. These synthetic data are integrated with mobility and epidemiological datasets to establish a reusable, privacy-preserving analytical framework. The authors develop a suite of six tools enabling tasks including hotspot detection, compliance monitoring, mobility analysis, and contact matrix estimation. Empirical validation demonstrates that privacy-preserving synthetic data can effectively and practically support pandemic surveillance and forecasting without compromising confidentiality.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Privacy-Enhancing Technologies
• Differential Privacy
• Public Health
• Data Sharing
• Pandemic Management

Innovation

• Differential Privacy
• Synthetic Data
• Privacy-Enhancing Technologies
• Public Health Informatics
• Epidemiological Forecasting