mlsanityData to 14 Oct 2026

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

arXiv 2405.00267 ↗First posted 1 May 2024cs.CR cs.CY cs.DScited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2024.

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Citing papers per year: 2024: 5; 2025: 8; 2026: 4.

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  1. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 13
  2. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 9
  3. 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
  4. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 5
  5. 2024Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysiscited by 3
  6. 2025dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generationcited by 3
  7. 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Trainingcited by 2
  8. 2025A Unified Framework for Adversary-Aware Differential Privacy Boundscited by 2
  9. 2025Differentially Private Wasserstein Barycenterscited by 2
  10. 2024Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliancecited by 1
  11. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  12. 2024Privacy-Computation trade-offs in Private Repetition and Metaselectioncited by 0
5 more
  1. 2025Understanding the Impact of Data Domain Extraction on Synthetic Data Privacycited by 0
  2. 2025Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamicscited by 0
  3. 2026From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculumcited by 0
  4. 2026Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentationcited by 0
  5. 2026Differentially Private Modeling of Disease Transmission within Human Contact Networkscited by 0