You are not logged in.

Privacy preserving in location data release: A differential privacy approach

Xiong,P, Zhu,T, Pan,L, Niu,W and Li,G 2014, Privacy preserving in location data release: A differential privacy approach. In Pham, D and Park, D (ed), PRICAI 2014: trends in artificial intelligence : 13th Pacific Rim international conference on artificial intelligence Gold Coast, QLD, Australia, December 1-5, 2014, proceedings, Springer, Berlin, Germany, pp.183-195, doi: 10.1007/978-3-319-13560-1.

Attached Files
Name Description MIMEType Size Downloads

Title Privacy preserving in location data release: A differential privacy approach
Author(s) Xiong,P
Zhu,TORCID iD for Zhu,T orcid.org/0000-0003-3411-7947
Pan,LORCID iD for Pan,L orcid.org/0000-0002-4691-8330
Niu,W
Li,GORCID iD for Li,G orcid.org/0000-0003-1583-641X
Title of book PRICAI 2014: trends in artificial intelligence : 13th Pacific Rim international conference on artificial intelligence Gold Coast, QLD, Australia, December 1-5, 2014, proceedings
Editor(s) Pham, D
Park, D
Publication date 2014
Series Lecture Notes in Artificial Intelligence
Chapter number 15
Total chapters 94
Start page 183
End page 195
Total pages 13
Publisher Springer
Place of Publication Berlin, Germany
Summary Communication devices with GPS chips allow people to generate large volumes of location data. However, location datasets have been confronted with serious privacy concerns. Recently, several privacy techniques have been proposed 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 datasets in a strict privacy notion, differential privacy. This algorithm 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 utility confirms an improved trade-off between the privacy and utility of released location data. The experimental results further suggest this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.
ISBN 9783319135601
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-13560-1
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071852

Document type: Book Chapter
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 249 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Fri, 27 Mar 2015, 16:21:22 EST

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.