LoPub: high-dimensional crowdsourced data publication with local differential privacy

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Title: LoPub: high-dimensional crowdsourced data publication with local differential privacy
Authors: Ren, X
Yu, C-M
Yu, W
Yang, S
Yang, X
McCann, JA
Yu, PS
Item Type: Journal Article
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.
Issue Date: 1-Sep-2018
Date of Acceptance: 27-Feb-2018
URI: http://hdl.handle.net/10044/1/62864
DOI: https://dx.doi.org/10.1109/TIFS.2018.2812146
ISSN: 1556-6013
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2151
End Page: 2166
Journal / Book Title: IEEE Transactions on Information Forensics and Security
Volume: 13
Issue: 9
Copyright Statement: © 2018 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.
Keywords: Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Local differential privacy
high-dimensional data
crowdsourced data
data publication
private data release
RANDOMIZED-RESPONSE
NOISE
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Local differential privacy
high-dimensional data
crowdsourced data
data publication
private data release
RANDOMIZED-RESPONSE
NOISE
08 Information And Computing Sciences
09 Engineering
Strategic, Defence & Security Studies
Publication Status: Published
Online Publication Date: 2018-03-05
Appears in Collections:Faculty of Engineering
Computing



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