Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms

TL;DR

Chorus is a scalable programming framework that integrates differential privacy mechanisms with production database systems, enabling practical deployment of complex privacy-preserving data analysis at large scale.

Contribution

The paper introduces Chorus, a novel framework that combines differential privacy mechanisms with high-performance DBMSs to achieve scalability and practicality.

Findings

Successfully deployed Chorus at Uber for real-world data analysis

Built scalable implementations of Weighted PINQ, MWEM, and matrix mechanism

Demonstrated improved scalability on real-world queries

Abstract

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.