http://dx.doi.org/10.1016/j.trc.2013.12.003">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Anonymizing trajectory data for passenger flow analysis

Language

English

Abstract

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 paper 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. © 2013 Elsevier Ltd.

Keywords

Anonymity, Data privacy, Passenger flow, Trajectory

Publication Date

2014

Source Publication Title

Transportation Research Part C: Emerging Technologies

Volume

39

Start Page

63

End Page

79

Publisher

Elsevier

ISSN (print)

0968090X

ISSN (electronic)

18792359

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