TMk-anonymity: Perturbation-based data anonymization method for improving effectiveness of secondary use

Taichi Nakamura, Hiroaki Nishi

研究成果: Conference contribution

抄録

The recent emergence of smartphones, cloud computing, and the Internet of Things has brought about the explosion of data creation. By collating and merging these enormous data with other information, services that use information become more sophisticated and advanced. However, at the same time, the consideration of privacy violations caused by such merging is indispensable. Various anonymization methods have been proposed to preserve privacy. The conventional perturbation-based anonymization method of location data adds comparatively larger noise, and the larger noise makes it difficult to utilize the data effectively for secondary use. In this research, to solve these problems, we first clarified the definition of privacy preservation and then propose TMfc-anonymity according to the definition.

本文言語English
ホスト出版物のタイトルProceedings
ホスト出版物のサブタイトルIECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3138-3143
ページ数6
ISBN(電子版)9781509066841
DOI
出版ステータスPublished - 2018 12 26
イベント44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 - Washington, United States
継続期間: 2018 10 202018 10 23

出版物シリーズ

名前Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

Conference

Conference44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
国/地域United States
CityWashington
Period18/10/2018/10/23

ASJC Scopus subject areas

  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 産業および生産工学
  • 制御と最適化

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