Explore a collaborative effort between Uber and Berkeley researchers to implement differential privacy at scale in this 21-minute conference talk from USENIX Enigma 2018. Delve into the challenges of applying differential privacy techniques to real-world industry requirements and learn about the open-source releases resulting from this partnership. Discover how the team addressed privacy risks, overcame limitations in current research, and developed practical solutions for large-scale data analysis. Examine the Chorus framework, which enforces differential privacy through query rewriting, and understand mechanisms like Elastic Sensitivity and Sample & Aggregate. Gain insights into experimental evaluations and the broad support for analytics queries in privacy-preserving environments.