Low-Information-Loss Anonymization of Trajectory Data Considering Map Information

Masahiro Hashimoto, Ryo Morishima, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Preserving an individual's privacy when publishing data are essential, and anonymization has been getting attention as the solution. When anonymizing data, it is necessary to contemplate the possibilities of linkage with other data which can lead to privacy violation. Trajectory data are one of the data, which contains personal data. Consequently, various anonymization methods of trajectory data have been considered by researchers. However, most research handle trajectory data as polylines connecting location data or as a sequence of location data. In other words, it lacks on considering the connection with map data. In this paper, we will consider the anonymization of trajectory data of moving users matched according to map data, which we will be calling pathing data. According to k-anonymity principle, data can be published if there are k of the same data. We will use k-anonymity principle to quantitively judge the risk of privacy violation and propose two methods that can fulfill the anonymization requirements with low data loss. The two methods are Map Matching to Node (MMtoN) and Map matching to Edge (MMtoE), which judges k-anonymity by segments of pathing data.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-504
Number of pages6
ISBN (Electronic)9781728156354
DOIs
Publication statusPublished - 2020 Jun
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 2020 Jun 172020 Jun 19

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2020-June

Conference

Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020
CountryNetherlands
CityDelft
Period20/6/1720/6/19

Keywords

  • big data
  • GPS
  • k-anonymity
  • map-matching
  • OpenStreetMap
  • trajectory

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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  • Cite this

    Hashimoto, M., Morishima, R., & Nishi, H. (2020). Low-Information-Loss Anonymization of Trajectory Data Considering Map Information. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings (pp. 499-504). [9152438] (IEEE International Symposium on Industrial Electronics; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE45063.2020.9152438