用差分隐私生成金融数据，助力疫情监测与决策。
Technical Case Study of Privacy-Enhancing Technologies (PETs) for Public Health
- 用差分隐私生成合成金融数据，保护隐私的同时保留时空特征。
- 生成数据能有效支持热点检测、出行分析等6项公共卫生任务。
- 为敏感数据共享提供可复用的隐私合规框架，适合政策研究者。
我们开展了一项关于公共健康领域隐私增强技术（PETs）的技术案例研究，旨在安全利用私营部门的敏感数据以实现社会价值，特别是用于疫情管理。项目采用差分隐私（DP）生成真实感强、隐私保护的合成金融交易数据，并将其与公开的公共卫生及移动数据结合。该方法成功克服了敏感金融信息用于研究与政策制定的共享障碍。分析表明，此类合成的DP保护数据具备显著的时空特征与预测能力。关键成果包括开发出六项可复用工具与框架，支持诊断性实时监测（如热点检测、疫情响应度监控）和预测性分析（如出行分析、接触矩阵估算），服务于流行病学决策。研究提出了推动隐私合规数据共享的最佳实践。
原文摘要 · Abstract (English)
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.
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