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

Xuebin Ren, Chia-mu Yu, Weiren Yu, Shusen Yang, Xinyu Yang, Julie A. McCann, Philip S. Yu

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)2151 - 2166
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Issue number9
Early online date5 Mar 2018
Publication statusPublished - 1 Sept 2018

Bibliographical note

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  • local differential privacy
  • high-dimensional data
  • crowdsourced data
  • data publication
  • data, crowdsourced data, data publication,


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