IoT-based Activities of Daily Living for Abnormal Behaviour Detection: Privacy Issues and Potential Countermeasures

Mustafa Mustafa, Alexandros Konios, Matias Garcia-Constantino

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Abstract

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 an example ADL system to demonstrate the effectiveness of our proposed countermeasures, before suggesting future research directions.
Original languageEnglish
Pages (from-to)90-95
Number of pages6
JournalIEEE Internet of Things Magazine
Volume4
Issue number3
Early online date6 Aug 2021
DOIs
Publication statusPublished - 30 Sep 2021

Keywords

  • Internet of Things (IoT)
  • IoT
  • IoT Security
  • Activity Recognition
  • IoT Privacy

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