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Dual Query: Practical Private Query Release for High Dimensional Data

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.
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Submitted on : Monday, March 5, 2018 - 2:05:55 PM
Last modification on : Wednesday, November 17, 2021 - 12:33:04 PM


  • HAL Id : hal-01702734, version 1


Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu. Dual Query: Practical Private Query Release for High Dimensional Data. Journal of Privacy and Confidentiality, 2016, 7 (Issue 2, Article N°4), pp.53 - 77. ⟨hal-01702734⟩



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