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

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), 2151 - 2166.


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: Engineering & Applied Sciences
Engineering & Applied 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,
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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
Yang, Shusen
Yang, Xinyu
McCann, Julie A.
Yu, Philip S.



Version: Accepted Version

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