Motion sensor data anonymization by time-frequency filtering

Noëlie Debs, Théo Jourdan, Ali Moukadem, Antoine Boutet, Carole Frindel

研究成果: Conference contribution

抄録

Recent advances in wireless actimetry sensors allow recognizing human real-time activities with mobile devices. Although the analysis of data generated by these devices can have many benefits for healthcare, these data also contains private information about users without their awareness and may even cause their re-identification. In this paper, we propose a privacy-preserving framework for activity recognition. The method consists of a two-step process. First, acceleration signals are encoded in the time-frequency domain by three different linear transforms. Second, we propose a method to anonymize the acceleration signals by filtering in the time-frequency domain. Finally, we evaluate our approach for the three different linear transforms with a neural network classifier by comparing the performances for activity versus identity recognition. We extensively study the validity of our framework with a reference dataset: results show an accurate activity recognition (85%) while limiting the re-identifation rate (32%). This represents a large utility improvement (19%) against a slight privacy decrease (10%) compared to state-of-the-art baseline.

本文言語English
ホスト出版物のタイトル28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ1707-1711
ページ数5
ISBN(電子版)9789082797053
DOI
出版ステータスPublished - 2021 1 24
外部発表はい
イベント28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
継続期間: 2020 8 242020 8 28

出版物シリーズ

名前European Signal Processing Conference
2021-January
ISSN(印刷版)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period20/8/2420/8/28

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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