Differential Privacy in Practice: Lessons Learned From 10 Years of Real-World Applications

Differential privacy (DP, a mathematical technique that adds controlled randomness to data to protect individual privacy while keeping data useful) is a widely-used method for protecting sensitive information, but putting it into practice in real-world systems has proven difficult. Researchers analyzed 21 actual deployments of differential privacy by major companies and institutions over the last ten years to understand what works and what doesn't.