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Conference Papers Year : 2021

Anonymizing motion sensor data through time-frequency domain

Abstract

The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
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Dates and versions

hal-03354723 , version 1 (26-09-2021)

Identifiers

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Pierre Rougé, Ali Moukadem, Alain Dieterlen, Antoine Boutet, Carole Frindel. Anonymizing motion sensor data through time-frequency domain. MLSP 2021 - Machine Learning for Signal Processing, Oct 2021, Queensland, Australia. pp.1-6, ⟨10.1109/MLSP52302.2021.9596442⟩. ⟨hal-03354723⟩
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