Journal
WIRELESS PERSONAL COMMUNICATIONS
Volume 126, Issue 3, Pages 2379-2401Publisher
SPRINGER
DOI: 10.1007/s11277-021-09323-0
Keywords
Internet of Things; Fog computing; Machine learning; Smart healthcare; Privacy; Sanitization
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Funding
- National Natural Science Foundation of China (NSFC) [61950410603]
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With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. However, conventional health systems cannot support the vast amount of biomedical data and hence it needs to be stored and shared through the cloud. Unfortunately, this data is prone to security threats and attacks.
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.
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