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A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing.

Authors :
Moqurrab, Syed Atif
Tariq, Noshina
Anjum, Adeel
Asheralieva, Alia
Malik, Saif U. R.
Malik, Hassan
Pervaiz, Haris
Gill, Sukhpal Singh
Source :
Wireless Personal Communications; Oct2022, Vol. 126 Issue 3, p2379-2401, 23p
Publication Year :
2022

Abstract

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called δ r sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that δ r sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09296212
Volume :
126
Issue :
3
Database :
Complementary Index
Journal :
Wireless Personal Communications
Publication Type :
Academic Journal
Accession number :
159631802
Full Text :
https://doi.org/10.1007/s11277-021-09323-0