DocumentCode :
2372445
Title :
Evaluation of privacy preserving algorithms using traffic knowledge based adversary models
Author :
Zhanbo Sun ; Bin Zan ; Ban, J. ; Gruteser, M. ; Peng Hao
Author_Institution :
Rensselaer Polytech. Inst., Troy, NY, USA
fYear :
2011
fDate :
5-7 Oct. 2011
Firstpage :
1075
Lastpage :
1082
Abstract :
By providing location traces of individual vehicles, mobile traffic sensors have quickly emerged as an important data source for traffic applications. In dealing with the privacy issues associated with this, researchers have been proposing different privacy protection algorithms. In this paper, we propose traffic-knowledge-based adversary models to attack privacy algorithms. By doing so, we can compare and evaluate different privacy algorithms in terms of both privacy protection and the convenience for traffic modeling. Results show that by having a relatively good privacy performance, the released datasets of both the 3.3 level of confusion entropy and the 0.1 individual likelihood can still be applied for a fine level of traffic applications.
Keywords :
data privacy; traffic engineering computing; confusion entropy; data source; mobile traffic sensors; privacy preserving algorithm; privacy protection algorithm; traffic application; traffic knowledge based adversary model; traffic modeling; traffic-knowledge-based adversary model; Data privacy; Entropy; Mathematical model; Measurement; Privacy; Trajectory; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on
Conference_Location :
Washington, DC
ISSN :
2153-0009
Print_ISBN :
978-1-4577-2198-4
Type :
conf
DOI :
10.1109/ITSC.2011.6083136
Filename :
6083136
Link To Document :
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