Privacy-preserving trajectory data publishing by local suppression

Rui CHEN, Benjamin C.M. Fung*, Noman Mohammed, Bipin C. Desai, Ke Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

206 Citations (Scopus)


The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low data utility in the resulting data and ineffective data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization. The framework allows the adoption of various data utility metrics for different data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a trajectory database, both being the foundation of many trajectory data mining tasks. Our experiments on both synthetic and real-life data sets suggest that the framework is effective and efficient to overcome the challenges in trajectory data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the data utility in anonymous trajectory data.

Original languageEnglish
Pages (from-to)83-97
Number of pages15
JournalInformation Sciences
Publication statusPublished - 10 May 2013

Scopus Subject Areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

User-Defined Keywords

  • Frequent sequence
  • Local suppression
  • Privacy preservation
  • Trajectory data


Dive into the research topics of 'Privacy-preserving trajectory data publishing by local suppression'. Together they form a unique fingerprint.

Cite this