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LoPub : high-dimensional crowdsourced data publication with local differential privacy
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Ren, Xuebin, Yu, Chia-Mu, Yu, Weiren, Yang, Shusen, Yang, Xinyu, McCann, Julie A. and Yu, Philip S. (2018) LoPub : high-dimensional crowdsourced data publication with local differential privacy. IEEE Transactions on Information Forensics and Security, 13 (9). pp. 2151-2166. doi:10.1109/TIFS.2018.2812146 ISSN 1556-6013.
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WRAP-LoPub-high-dimensional-crowdsourced-data-local-privacy-Yu-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1902Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TIFS.2018.2812146
Abstract
High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Library of Congress Subject Headings (LCSH): | Privacy, Right of, Data protection, Crowdsourcing, Expectation-maximization algorithms , Computer security | ||||
Journal or Publication Title: | IEEE Transactions on Information Forensics and Security | ||||
Publisher: | IEEE | ||||
ISSN: | 1556-6013 | ||||
Official Date: | 5 March 2018 | ||||
Dates: |
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Volume: | 13 | ||||
Number: | 9 | ||||
Page Range: | pp. 2151-2166 | ||||
DOI: | 10.1109/TIFS.2018.2812146 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 31 January 2020 | ||||
Date of first compliant Open Access: | 12 February 2020 |
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