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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
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) | ||||||||||||||||||
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| 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 | ||||||||||||||||||
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| Date of first compliant deposit: | 10 April 2018 | ||||||||||||||||||
| 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 | ||||||||||||||||||
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| 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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