LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy


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 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 realworld 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 SVM and random forest classification, respectively.

Publication DOI: https://doi.org/10.1109/TIFS.2018.2812146
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 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.
Uncontrolled Keywords: local differential privacy,high-dimensional data,crowdsourced data, data publication, data, crowdsourced data, data publication,
Publication ISSN: 1556-6021
Last Modified: 17 Apr 2024 07:15
Date Deposited: 09 Mar 2018 13:45
Full Text Link:
Related URLs: http://ieeexplo ... cument/8306916/ (Publisher URL)
PURE Output Type: Article
Published Date: 2018-09-01
Published Online Date: 2018-03-05
Accepted Date: 2018-03-01
Authors: Ren, Xuebin
Yu, Chia-mu
Yu, Weiren (ORCID Profile 0000-0002-1082-9475)
Yang, Shusen
Yang, Xinyu
McCann, Julie A.
Yu, Philip S.



Version: Accepted Version

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