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Toward privacy in IoT mobile devices for activity recognition


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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Dates and versions

hal-01941453 , version 1 (04-12-2018)


  • 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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