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

Masahiro Hashimoto, Ryo Morishima, Hiroaki Nishi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ499-504
ページ数6
ISBN(電子版)9781728156354
DOI
出版ステータスPublished - 2020 6
イベント29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
継続期間: 2020 6 172020 6 19

出版物シリーズ

名前IEEE International Symposium on Industrial Electronics
2020-June

Conference

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

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学

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