Differential Privacy and Risk Metrics for Safe Data Sharing

This whitepaper discusses the concepts of differential privacy and risk metrics in the context of safe data sharing, particularly in healthcare. It outlines how sensitive health data can be reused responsibly to enhance healthcare services while safeguarding individual privacy. The document emphasizes the importance of disclosure risk metrics, which allow for the transformation and reuse of data without compromising the integrity of personal information. It details the technical privacy models that assess disclosure risks and the role of differential privacy in protecting individual contributions. The paper also examines various risks associated with data sharing, including prosecutor, journalist, and marketer risks, and how these can be managed through statistical methods and benchmarks. Additionally, it presents examples of how randomization techniques can be applied to health data to ensure anonymity while maintaining truthful statistics. The overall objective is to provide a framework for the safe and responsible use of health-related data.