Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ren:2018:10.1109/TIFS.2018.2812146,
author = {Ren, X and Yu, C-M and Yu, W and Yang, S and Yang, X and McCann, JA and Yu, PS},
doi = {10.1109/TIFS.2018.2812146},
journal = {IEEE Transactions on Information Forensics and Security},
pages = {2151--2166},
title = {LoPub: high-dimensional crowdsourced data publication with local differential privacy},
url = {http://dx.doi.org/10.1109/TIFS.2018.2812146},
volume = {13},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Ren,X
AU - Yu,C-M
AU - Yu,W
AU - Yang,S
AU - Yang,X
AU - McCann,JA
AU - Yu,PS
DO - 10.1109/TIFS.2018.2812146
EP - 2166
PY - 2018///
SN - 1556-6013
SP - 2151
TI - LoPub: high-dimensional crowdsourced data publication with local differential privacy
T2 - IEEE Transactions on Information Forensics and Security
UR - http://dx.doi.org/10.1109/TIFS.2018.2812146
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000431896200002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/62864
VL - 13
ER -