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Marginal release under local differential privacy

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Cormode, Graham, Kulkarni, Tejas M. and Srivastava, D. (2018) Marginal release under local differential privacy. In: 2018 ACM SIGMOD/PODS, Houston, TX, USA, 10-15 Jun 2018. Published in: SIGMOD '18 Proceedings of the 2018 International Conference on Management of Data pp. 131-146. ISBN 9781450347037. doi:10.1145/3183713.3196906

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Official URL: https://doi.org/10.1145/3183713.3196906

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Abstract

Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Algorithms, Sampling (Statistics), Machine learning, Fourier transformations, Computer security
Journal or Publication Title: SIGMOD '18 Proceedings of the 2018 International Conference on Management of Data
Publisher: ACM
ISBN: 9781450347037
Official Date: 6 April 2018
Dates:
DateEvent
6 April 2018Accepted
Page Range: pp. 131-146
DOI: 10.1145/3183713.3196906
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
ERC-2014-CoG 647557European Research Councilhttp://viaf.org/viaf/130022607
EP/N510129/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
618202H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663
UNSPECIFIEDAT & T (Firm)http://viaf.org/viaf/164568711
UNSPECIFIEDAlan Turing Institutehttp://dx.doi.org/10.13039/100012338
Is Part Of:
Conference Paper Type: Paper
Title of Event: 2018 ACM SIGMOD/PODS
Type of Event: Conference
Location of Event: Houston, TX, USA
Date(s) of Event: 10-15 Jun 2018
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