PAM: An efficient and privacy-aware monitoring framework for continuously moving objects

Haibo HU*, Jianliang XU, Dik Lun Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Efficiency and privacy are two fundamental issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses both issues. The framework distinguishes itself from the existing work by being the first to holistically address the issues of location updating in terms of monitoring accuracy, efficiency, and privacy, particularly, when and how mobile clients should send location updates to the server. Based on the notions of safe region and most probable result, PAM performs location updates only when they would likely alter the query results. Furthermore, by designing various client update strategies, the framework is flexible and able to optimize accuracy, privacy, or efficiency. We develop efficient query evaluation/reevaluation and safe region computation algorithms in the framework. The experimental results show that PAM substantially outperforms traditional schemes in terms of monitoring accuracy, CPU cost, and scalability while achieving close-to-optimal communication cost.

Original languageEnglish
Article number4840343
Pages (from-to)404-419
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume22
Issue number3
DOIs
Publication statusPublished - Mar 2010

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Location-dependent and sensitive
  • Mobile applications
  • Spatial databases

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