  Differential privacy is fast becoming the gold standard in enabling
statistical analysis of data while protecting the privacy of individuals.
However, practical use of differential privacy still lags behind research
progress because research prototypes cannot satisfy the scalability
requirements of production deployments. To address this challenge, we present
Chorus, a framework for building scalable differential privacy mechanisms which
is based on cooperation between the mechanism itself and a high-performance
production database management system (DBMS). We demonstrate the use of Chorus
to build the first highly scalable implementations of complex mechanisms like
Weighted PINQ, MWEM, and the matrix mechanism. We report on our experience
deploying Chorus at Uber, and evaluate its scalability on real-world queries.
