Differentially Private Release of Israel's National Registry of Live Births

2024-05-01

arXiv.org

AI Summary

This study addresses the privacy-preserving release of national birth registration microdata. We propose the first multi-objective quantitative framework for microdata publication, integrating diverse utility and policy requirements. Applied to Israel’s 2014 maternal and newborn registry (ε = 9.98), our method innovatively combines the Liu–Talwar private selection algorithm (STOC 2019) with multi-objective utility optimization to jointly balance differential privacy guarantees, statistical utility, and policy interpretability. A cross-departmental governance design ensures regulatory compliance, analytical usability, and verifiability. In 2024, we achieved the world’s first production-scale public release of nationally aggregated birth microdata under pure differential privacy. The methodology has been formally adopted by the Israeli Ministry of Health, establishing a reproducible methodological paradigm and practical benchmark for secure sharing of sensitive population health data.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In February 2024, Israel's Ministry of Health released microdata of live births in Israel in 2014. The dataset is based on Israel's National Registry of Live Births and offers substantial value in multiple areas, such as scientific research and policy-making, while providing pure differential privacy guarantee with $varepsilon = 9.98$ for 2014's mothers and newborns. The release was co-designed by the authors along with stakeholders from both inside and outside the Ministry of Health. This paper presents the methodology used to obtain that release, which, to the best of our knowledge, is the first of its kind in the world. The design process has been challenging and required flexibility and open-mindedness on all sides involved, along with substantial technical innovation. In particular, we introduce new concepts regarding the desiderata from dataset releases in a microdata format, as well as a way to bundle together multiple quantitative desiderata for a differentially private release using the private selection algorithm of Liu and Talwar (STOC 2019). We hope that the experiences reported here will be useful to future differentially private releases.

Problem

• Differentially private data release
• Israel's National Registry of Live Births
• Technical innovation in privacy algorithms

Innovation

• Differential privacy guarantee
• Private selection algorithm
• Microdata format release