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

Moqurrab, Syed Atif1; Tariq, Noshina2; Anjum, Adeel1,3; Asheralieva, Alia3; Malik, Saif U. R.4; Malik, Hassan5; Pervaiz, Haris6; Gill, Sukhpal Singh7

2022-08-01

10.1007/s11277-021-09323-0

WIRELESS PERSONAL COMMUNICATIONS   Impact Factor & Quartile

0929-6212

1572-834X

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 delta(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 delta(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.

Internet of Things Fog computing Machine learning Smart healthcare Privacy Sanitization

SCI ; EI

English

Others

National Natural Science Foundation of China (NSFC)[61950410603]

Telecommunications

Telecommunications

WOS:000847660400001

SPRINGER

20223612686664

Diagnosis ; Fog ; Health care ; Internet of things ; Long short-term memory ; Medical applications ; Privacy-preserving techniques

Atmospheric Properties:443.1 ; Medicine and Pharmacology:461.6 ; Health Care:461.7 ; Telecommunication; Radar, Radio and Television:716 ; Telephone Systems and Related Technologies; Line Communications:718 ; Data Communication, Equipment and Techniques:722.3 ; Digital Computers and Systems:722.4 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2

COMPUTER SCIENCE

Web of Science

1.COMSATS Univ, Dept Comp Sci, Islamabad, Pakistan
2.Shaheed Zulfiqar Ali Bhutto Inst Sci & Technol, Dept Comp Sci, Islamabad, Pakistan
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
4.Cybernet AS Estonia, Tallinn, Estonia
5.Edge Hill Univ, Dept Comp Sci, Ormskirk, England
6.Univ Lancaster, Sch Comp & Commun, Lancaster, Lancs, England
7.Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England

Moqurrab, Syed Atif,Tariq, Noshina,Anjum, Adeel,et al. A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing[J]. WIRELESS PERSONAL COMMUNICATIONS,2022.

Moqurrab, Syed Atif.,Tariq, Noshina.,Anjum, Adeel.,Asheralieva, Alia.,Malik, Saif U. R..,...&Gill, Sukhpal Singh.(2022).A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing.WIRELESS PERSONAL COMMUNICATIONS.

Moqurrab, Syed Atif,et al."A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing".WIRELESS PERSONAL COMMUNICATIONS (2022).