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Toward privacy in IoT mobile devices for activity recognition

Théo Jourdan 1 Antoine Boutet 2 Carole Frindel 3 
2 PRIVATICS - Privacy Models, Architectures and Tools for the Information Society
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Lyon
3 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastreamcanhavemanybenefitsfromahealthpointofview,itcanalsoleadtoprivacy 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 rely on a carefully features extraction scheme in both temporal and frequency domainandapplyageneralisation-basedapproachonfeaturesleadingtore-identify users. 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.
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Submitted on : Tuesday, December 4, 2018 - 2:38:37 PM
Last modification on : Wednesday, March 9, 2022 - 3:29:31 PM
Long-term archiving on: : Tuesday, March 5, 2019 - 12:59:00 PM


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  • HAL Id : hal-01941453, version 1


Théo Jourdan, Antoine Boutet, Carole Frindel. Toward privacy in IoT mobile devices for activity recognition. Privacy Preserving Machine Learning NeurIPS 2018 Workshop, Dec 2018, Montréal, Canada. pp.1-6. ⟨hal-01941453⟩



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