We present Chorus, a system with a novel architecture for providing differential privacy for statistical SQL queries. The key to our approach is to embed a differential privacy mechanism into the query before execution so the query automatically enforces differential privacy on its output. Chorus is compatible with any SQL database that supports standard math functions, requires no user modifications to the database or queries, and simultaneously supports many differential privacy mechanisms. To the best of our knowledge, no existing system provides these capabilities. We demonstrate our approach using four general-purpose differential privacy mechanisms. In the first evaluation of its kind, we use Chorus to evaluate these four mechanisms on real-world queries and data. The results demonstrate that our approach supports 93.9 of statistical queries in our corpus, integrates with a production DBMS without any modifications, and scales to hundreds of millions of records. Chorus is currently being deployed at Uber for its internal analytics tasks. Chorus represents a significant part of the company's GDPR compliance efforts, and can provide both differential privacy and access control enforcement. In this capacity, Chorus processes more than 10,000 queries per day.
A number of systems for enforcing differential privacy have been developed. PINQ @cite_53 supports a LINQ-based query language, and implements the Laplace mechanism with a measure of global sensitivity. Weighted PINQ @cite_63 extends PINQ to weighted datasets, and implements a specialized mechanism for that setting.
@cite_53 @cite_63
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We report on the design and implementation of the Privacy Integrated Queries (PINQ) platform for privacy-preserving data analysis. PINQ provides analysts with a programming interface to unscrubbed data through a SQL-like language. At the same time, the design of PINQ's analysis language and its careful implementation provide formal guarantees of differential privacy for any and all uses of the platform. PINQ's unconditional structural guarantees require no trust placed in the expertise or diligence of the analysts, substantially broadening the scope for design and deployment of privacy-preserving data analysis, especially by non-experts. We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes. This paper details the data analysis platform wPINQ, which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (e.g., counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.