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A differentially private algorithm for location data release

Xiong, Ping, Zhu, Tianqing, Niu, Wenjia and Li, Gang 2016, A differentially private algorithm for location data release, Knowledge and information systems, vol. 47, no. 3, pp. 647-669, doi: 10.1007/s10115-015-0856-1.

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Title A differentially private algorithm for location data release
Author(s) Xiong, Ping
Zhu, TianqingORCID iD for Zhu, Tianqing orcid.org/0000-0003-3411-7947
Niu, Wenjia
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Journal name Knowledge and information systems
Volume number 47
Issue number 3
Start page 647
End page 669
Total pages 23
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-06
ISSN 0219-1377
0219-3116
Summary The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.
Language eng
DOI 10.1007/s10115-015-0856-1
Field of Research 080109 Pattern Recognition and Data Mining
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076104

Document type: Journal Article
Collection: School of Information Technology
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