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Privacy Preservation in High-dimensional Trajectory Data for Passenger Flow Analysis


Privacy Preservation in High-dimensional Trajectory Data for Passenger Flow Analysis

Ghasemzadeh, Moein (2013) Privacy Preservation in High-dimensional Trajectory Data for Passenger Flow Analysis. Masters thesis, Concordia University.

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Ghasemzadeh_MASc_F2013.pdf - Accepted Version
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The increasing use of location-aware devices provides many opportunities for analyzing and mining human mobility. The trajectory of a person can be represented as a sequence of visited locations with different timestamps. Storing, sharing, and analyzing personal trajectories may pose new privacy threats. Previous studies have shown that employing traditional privacy models and anonymization methods often leads to low information quality in the resulting data. In this thesis we propose a method for achieving anonymity in a trajectory database while preserving the information to support effective passenger flow analysis. Specifically, we first extract the passenger flowgraph, which is a commonly employed representation for modeling uncertain moving objects, from the raw trajectory data. We then anonymize the data with the goal of minimizing the impact on the flowgraph. Extensive experimental results on both synthetic and real-life data sets suggest that the framework is effective to overcome the special challenges in trajectory data anonymization, namely, high dimensionality, sparseness, and sequentiality.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Ghasemzadeh, Moein
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:September 2013
Thesis Supervisor(s):Fung, Benjamin C. M. and Awasthi, Anjali
ID Code:977774
Deposited On:19 Nov 2013 20:28
Last Modified:18 Jan 2018 17:45
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