Privacy integrated queries: an extensible platform for privacy-preserving data analysis
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.
|Frank McSherry is the Chief Scientist at Materialize, Inc., where he works on interactive and incremental data processing. Frank was previously at the Systems Group at ETH Zürich where he worked with students on timely and differential dataflow, and even further back at MSR Silicon Valley where he worked on the Naiad project and on Differential Privacy. Frank is perhaps best known for applying his undergraduate education to big data problems.|