What if there was a way to precisely predict the risk of a major data breach when sharing information? In this illuminating episode of Secure Talk, Justin Beals sits down with Simson Garfinkel, renowned computer scientist, journalist, and author who helped implement differential privacy for the U.S. Census Bureau's 2020 census. As a fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and the IEEE, and with leadership positions at both the Department of Homeland Security and U.S. Census Bureau, Garfinkel offers unparalleled insights into how mathematics is creating an entirely new frontier in privacy protection in his new book “Differential Privacy”.
Differential privacy is a reliable mathematical framework that quantifies privacy risk or the potential for a major breach. It can transform how organizations understand, measure, and control data exposure. Yet most security, compliance, and legal professionals haven't grasped its revolutionary implications for measuring and predicting a major privacy breach.
Join Justin and Simson as they reveal:
- How differential privacy allows organizations to calculate privacy risk with mathematical precision
- Why this new field of privacy research eliminates guesswork when combining and distributing sensitive data
- The revolutionary balance between data utility and privacy protection that was previously impossible
- How forward-thinking organizations are using these mathematical formula to unlock data value safely
This isn't abstract theory – it's a practical revolution in how we approach data sharing. Garfinkel, who literally wrote the book on "Differential Privacy," shares real-world examples from his work with the U.S. Census Bureau, where differential privacy enabled the release of valuable population data while mathematically predicting individual privacy. In his book Simson breaks down complex mathematical concepts into clear, actionable insights for security leaders, compliance officers, and legal counsel.
Listen now to discover how differential privacy is creating a future where data sharing decisions are based on mathematical certainty rather than best guesses and crossed fingers.
Link to Simson's book: https://mitpress.mit.edu/9780262551656/differential-privacy/