Dual query: practical private query release for high dimensional data

Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Roth Aaron, Zhiwei Steven Wu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    8 Citations (Scopus)

    Abstract

    We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily has worst-case complexity exponential in the dimension of the data. However, our algorithm packages the computationally hard step into a concisely defined integer program, which can be solved non-privately using standard solvers. We prove accuracy and privacy theorems for our algorithm, and then demonstrate experimentally that our algorithm performs well in practice. For example, our algorithm can efficiently and accurately answer millions of queries on the Netflix dataset, which has over 17,000 attributes; this is an improvement on the state of the art by multiple orders of magnitude.

    Original languageEnglish
    Title of host publication31st International Conference on Machine Learning, ICML 2014
    PublisherInternational Machine Learning Society
    Pages2908-2916
    Number of pages9
    Volume4
    ISBN (Print)9781634393973
    Publication statusPublished - 2014
    Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
    Duration: 21 Jun 201426 Jun 2014
    http://icml.cc/2014/

    Publication series

    NameJournal of Machine Learning Research
    Volume32

    Conference

    Conference31st International Conference on Machine Learning, ICML 2014
    Abbreviated titleICML 2014
    CountryChina
    CityBeijing
    Period21/06/1426/06/14
    Internet address

    Cite this

    Gaboardi, M., Arias, E. J. G., Hsu, J., Aaron, R., & Wu, Z. S. (2014). Dual query: practical private query release for high dimensional data. In 31st International Conference on Machine Learning, ICML 2014 (Vol. 4, pp. 2908-2916). (Journal of Machine Learning Research ; Vol. 32). International Machine Learning Society . http://jmlr.org/proceedings/papers/v32/gaboardi14.html