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Differential private POI queries via Johnson-Lindenstrauss transform

Yang, Mengmeng, Zhu, Tianqing, Liu, Bo, Xiang, Yang and Zhou, Wanlei 2018, Differential private POI queries via Johnson-Lindenstrauss transform, IEEE access, vol. 6, pp. 29685-29699, doi: 10.1109/ACCESS.2018.2840726.

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Title Differential private POI queries via Johnson-Lindenstrauss transform
Author(s) Yang, Mengmeng
Zhu, TianqingORCID iD for Zhu, Tianqing orcid.org/0000-0003-3411-7947
Liu, BoORCID iD for Liu, Bo orcid.org/0000-0002-3603-6617
Xiang, Yang
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Journal name IEEE access
Volume number 6
Start page 29685
End page 29699
Total pages 15
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-05-27
ISSN 2169-3536
Keyword(s) differential privacy
Johnson Lindenstrauss transform
location privacy
LBS
science & technology
technology
computer science, information systems
engineering, electrical & electronic
telecommunications
computer science
engineering
Summary The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user's locations but other sensitive information, such as the user's interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user's location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.
Language eng
DOI 10.1109/ACCESS.2018.2840726
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30111996

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.