Toward privacy in IoT mobile devices for activity recognition

Théo Jourdan, Antoine Boutet, Carole Frindel

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

Abstract

Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

Original languageEnglish
Title of host publicationProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, Mobiquitous 2018
PublisherAssociation for Computing Machinery
Pages155-165
Number of pages11
ISBN (Electronic)9781450360937
DOIs
Publication statusPublished - 2018 Nov 5
Externally publishedYes
Event15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018 - New York, United States
Duration: 2018 Nov 52018 Nov 7

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018
CountryUnited States
CityNew York
Period18/11/518/11/7

Keywords

  • Activity recognition
  • IoT healthcare
  • Privacy

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Jourdan, T., Boutet, A., & Frindel, C. (2018). Toward privacy in IoT mobile devices for activity recognition. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018 (pp. 155-165). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3286978.3287009