How can we apply differential privacy to real-world scenarios? How do you go about algorithmic design? Is there a conflict between data minimization and differential privacy? Can you solve for personal data finding its way into machine learning models? Where can a young professional find resources to dive deeper?

References:

* Daniel Simmons-Marengo on LinkedIn

* OpenDP

* Some takeaways from PEPR’24 (USENIX Conference on Privacy Engineering Practice and Respect 2024)

* Damien Desfontaines: Differential Privacy in Data Clean Rooms (Masters of Privacy, January 2024)

* NIST Guidelines for Evaluating Differential Privacy Guarantees (March 2025)

* Peter Craddock: EDPS v SRB, the relative nature of personal data, processors, transparency, impact on MarTech and AdTech (Masters of Privacy, September 2025)

* Katharine Jarmul: Demystifying Privacy Enhancing Technologies (Masters of Privacy, October 2023)

* Sunny Kang: Machine Learning meets Privacy Enhancing Technologies (Masters of Privacy, February 2023)

* How GDPR changes the rules for research (Gabe Maldoff, IAPP blog, 2016)

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Daniel Simmons-Marengo: Differential Privacy in practice