This paper presents the methodology and process for releasing Israel's National Registry of Live Births in 2014 while ensuring differential privacy. The release was co-designed with stakeholders from the Ministry of Health and other organizations. The dataset, containing 167K records of singleton live births, was processed to protect the privacy of mothers and newborns. The authors used differential privacy as the formal measure of privacy loss, achieving a privacy loss budget of ε = 9.98. They employed the private selection algorithm of Liu and Talwar to bundle multiple steps, including data transformation, model generation, hyperparameter selection, and evaluation. The model generation algorithm used was PrivBayes. The evaluation was based on a set of acceptance criteria, which were disclosed approximately to provide overall differential privacy guarantees. The paper also discusses challenges and future directions for similar releases. The released dataset, along with documentation and code, is publicly available. The project demonstrates the feasibility of using differential privacy for releasing government data, particularly in the medical domain, and highlights the importance of incorporating context-specific requirements into the release process.This paper presents the methodology and process for releasing Israel's National Registry of Live Births in 2014 while ensuring differential privacy. The release was co-designed with stakeholders from the Ministry of Health and other organizations. The dataset, containing 167K records of singleton live births, was processed to protect the privacy of mothers and newborns. The authors used differential privacy as the formal measure of privacy loss, achieving a privacy loss budget of ε = 9.98. They employed the private selection algorithm of Liu and Talwar to bundle multiple steps, including data transformation, model generation, hyperparameter selection, and evaluation. The model generation algorithm used was PrivBayes. The evaluation was based on a set of acceptance criteria, which were disclosed approximately to provide overall differential privacy guarantees. The paper also discusses challenges and future directions for similar releases. The released dataset, along with documentation and code, is publicly available. The project demonstrates the feasibility of using differential privacy for releasing government data, particularly in the medical domain, and highlights the importance of incorporating context-specific requirements into the release process.