Real-time secure health surveillance for smarter health communities
Abstract
Pervasive healthcare services with smart decision making capability and ubiquitous communication technologies can forge future smart communities. Real-time health surveillance for early detection of life-threatening diseases through advanced sensing and communication technology can provide better treatment, reduce medical expenses and save lives of community residents (i.e., patients). However, the assurance of data privacy is the prime concern for such smart health technologies. This research aims to describe a privacy-preserving cloud-based system for real-time health surveillance through change detection of multiple vital health signs of smart community members. Vital signs data generated from IoT-enabled wearable devices are processed in real-time in a cloud environment. This article focuses on the development of a predictive model for the smart community considering the sensitivity of data processing in a third-party environment (e.g., cloud computing). We developed a vital sign change detection system using Holt's linear trend method (to enable prediction of data with trends) where fully homomorphic encryption is adapted to perform computations on an encrypted domain that can ensure data privacy. Moreover, to reduce the overhead of the fully homomorphic encryption method over large medical data we introduced a parallel approach for encrypted computations using a MapReduce algorithm of Apache Hadoop. We demonstrated the proposed model by evaluating some case studies for…
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IoMT systems are excellent candidates for preventing, predicting, and monitoring emerging infectious diseases like COVID-19.
“are excellent candidates for preventing, predicting, and monitoring emerging infectious diseases (Christaki, 2015; Alabdulatif et al., 2019) like COVID-19.”
From IoMT amid COVID-19 pandemic: Application, architecture, technology, and security (2021) · cites this paper as Alabdulatif et al., 2019
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IoMT amid COVID-19 pandemic: Application, architecture, technology, and security
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237 · see citing works
- Authors
- Alabdulatif, A., Khalil, I., Forkan, A. R. M., & Atiquzzaman, M.
- Affiliations
- RMIT University (AU)
- Swinburne University of Technology (AU)
- University of Oklahoma (US)
- Venue
- IEEE Communications Magazine
- Published
- 2019-01
- Volume
- 57
- Issue
- 1
- Pages
- 122-129
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2906758593
- Cited by
- 54
- References
- 15
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 11 · 2022: 10 · 2021: 9 · 2020: 9 · 2019: 2
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- smart communities, privacy-preserving cloud computing, fully homomorphic encryption, wearable devices, Internet of Things, MapReduce, Hadoop, data privacy
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