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Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

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Published:30 December 2020Publication History
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Abstract

The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated 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 article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

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        cover image ACM Transactions on Computing for Healthcare
        ACM Transactions on Computing for Healthcare  Volume 2, Issue 1
        Special Issue on Wearable Technologies for Smart Health: Part 2 and Regular Papers
        January 2021
        204 pages
        ISSN:2691-1957
        EISSN:2637-8051
        DOI:10.1145/3446563
        Issue’s Table of Contents

        Copyright © 2020 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 30 December 2020
        • Revised: 1 August 2020
        • Accepted: 1 August 2020
        • Received: 1 August 2019
        Published in health Volume 2, Issue 1

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