| Title | Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Johnson, N., Near, J. P., Hellerstein, J. M., Song, D. |
| Conference Name | 2020 IEEE European Symposium on Security and Privacy (EuroS P) |
| Date Published | sep |
| Keywords | Chorus, composability, data privacy protection, data protection, database management systems, DBMS, Differential privacy, high-performance production database management system, Human Behavior, matrix mechanism, MWEM, privacy, Programming, pubcrawl, query rewriting, real-world queries, Resiliency, Scalability, scalability requirements, scalable differential privacy mechanisms, security, SQL queries, statistical analysis, weighted PINQ |
| 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. |
| DOI | 10.1109/EuroSP48549.2020.00041 |
| Citation Key | johnson_chorus_2020 |
Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms