Anonymizing trajectory data for passenger flow analysis

Moein Ghasemzadeh, Benjamin C.M. Fung*, Rui CHEN, Anjali Awasthi

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

48 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)63-79
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Feb 2014

Scopus Subject Areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

User-Defined Keywords

  • Anonymity
  • Data privacy
  • Passenger flow
  • Trajectory


Dive into the research topics of 'Anonymizing trajectory data for passenger flow analysis'. Together they form a unique fingerprint.

Cite this