IoT-based activities of daily living for abnormal behavior detection: privacy issues and potential countermeasures

Mustafa, M.A., Konios, A. ORCID: 0000-0001-5281-1911 and Garcia-Constantino, M., 2021. IoT-based activities of daily living for abnormal behavior detection: privacy issues and potential countermeasures. IEEE Internet of Things Magazine, 4 (3), pp. 90-95. ISSN 2576-3180

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Activities of Daily Living (ADL) systems have been playing an important role in assessing and monitoring the quality of life of elderly people for many years. With the recent advancement and integration of Internet of Things (IoT) devices within the ADL systems, the number and quality of services offered has increased significantly. One of these vital services is abnormal behaviour detection based on the data collected from IoT devices within smart homes. However, the IoT data collected could have enormous privacy implications on smart home users if the data is not handled properly. We address this issue by analysing a generic ADL system for abnormal behaviour detection, including its entities and their interactions. We highlight three major privacy issues: (i) identity privacy, (ii) data confidentiality, and (iii) metadata data leakage. These issues are particularly relevant to ADL systems and we propose potential countermeasures to tackle them. Finally, we sketch a privacy-preserving version of a state-of-the-art ADL system to demonstrate the effectiveness of our proposed countermeasures, before suggesting future research directions.

Item Type: Journal article
Publication Title: IEEE Internet of Things Magazine
Creators: Mustafa, M.A., Konios, A. and Garcia-Constantino, M.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: September 2021
Volume: 4
Number: 3
ISSN: 2576-3180
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 14 Nov 2022 16:14
Last Modified: 06 Aug 2023 03:00

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