Frank McSherry
Abstract
Privacy Integrated Queries (PINQ) is an extensible data analysis platform designed to provide unconditional privacy guarantees for the records of the underlying data sets. PINQ provides analysts with access to records through an SQL-like declarative language (LINQ) amidst otherwise arbitrary C# code. 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 guarantees require no trust placed in the expertise or diligence of the analysts, broadening the scope for design and deployment of privacy-preserving data analyses, especially by privacy nonexperts.
- Adam, N.R., Wortmann, J.C. Security-control methods for statistical databases: A comparative study, ACM Comput. Surv., 21, 4 (1989), 515--556. Google Scholar
Digital Library
- Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K. Privacy, accuracy, and consistency too: a holistic solution to contingency table release, in PODS (2007), 273--282. Google ScholarDigital Library
- Barbaro, M., Zeller Jr., T. A face is exposed for AOL searcher no. 4417749, The New York Times, August 9, 2006.Google Scholar
- Blum, A., Dwork, C., McSherry, F., Nissim, K. Practical privacy: The SuLQ framework, in PODS (2005), 128--138. Google ScholarDigital Library
- Dwork, C. Differential privacy, in ICALP (2006), 1--12. Google ScholarDigital Library
- Dwork, C. A firm foundation for private data analysis, Communications of the ACM, Association for Computing Machinery, Inc., 2010. Google ScholarDigital Library
- Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M., Our data, ourselves: Privacy via distributed noise generation, in EUROCRYPT (2006), 486--503. Google ScholarDigital Library
- Dwork, C., McSherry, F., Nissim, K., Smith, A. Calibrating noise to sensitivity in private data analysis, in TCC (2006), 265--284. Google ScholarDigital Library
- Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D. Dryad: distributed data-parallel programs from sequential building blocks, in EuroSys. ACM (2007), 59--72. Google ScholarDigital Library
- McSherry, F. Privacy integrated queries: an extensible platform for privacy-preserving data analysis, in SIGMOD Conference (2009), 19--30. Google ScholarDigital Library
- McSherry, F., Talwar, K. Mechanism design via differential privacy, in FOCS (2007), 94--103. Google ScholarDigital Library
- McSherry, F., Talwar, K. Synthetic data via differential privacy, Manuscript.Google Scholar
- Mirkovic, J. Privacy-safe nework trace sharing via secure queries, in NDA (2008). Google ScholarDigital Library
- Roy, I., Setty, S.T., Kilzer, A., Shmatikov, V., Witchel, E. Airavat: Security and privacy for mapreduce, in NSDI Conference (2010). Google ScholarDigital Library
- Yu, Y., Isard, M., Fetterly, D., Budiu, M., Erlingsson, U, Gunda, P.K., Currey, J. DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language, in OSDI (2008). Google ScholarDigital Library
Index Terms
- Privacy integrated queries: an extensible platform for privacy-preserving data analysis
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