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

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arxiv 2405.00267 v2 pith:XY2633SZ submitted 2024-05-01 cs.CR cs.CYcs.DS

Shlomi Hod , Ran Canetti This is my paper
classification cs.CRcs.CYcs.DS
keywords israelprivatereleasebirthsdifferentiallylivealongdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. "We Need a Standard": Toward an Expert-Informed Privacy Label for Differential Privacy

    cs.CR 2025-07 conditional novelty 7.0 of 10

    Twelve DP experts converged on a core set of parameters, including epsilon, delta, and the unit of privacy, that a standardized differential privacy label should disclose to technical audiences.

  2. Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics

    cs.LG 2025-06 reject novelty 5.0 of 10

    A differentially private particle-gradient algorithm generates continuous-time synthetic trajectories from one snapshot per person, but its headline recovery rate applies only to a non-private infinite-particle idealization.

  3. Synthetic Tabular Data: Methods, Attacks and Defenses

    cs.LG 2025-06 conditional novelty 1.0 of 10

    A review of tabular synthetic data generation, privacy attacks, and defenses, whose central message is that synthetic data alone does not guarantee privacy.

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