Internet of Healthcare Things: Machine Learning for Security and Privacy (Machine Learning in Biomedical Science and Healthcare Informatics)

✍ Scribed by Kavita Sharma (editor), Yogita Gigras (editor), Vishnu Sharma (editor), D. Jude Hemanth (editor), Ramesh Chandra Poonia (editor)

• Publisher: Wiley-Scrivener
• Year: 2022
• Tongue: English
• Leaves: 295
• Edition: 1
• Category: Library

✦ Synopsis

INTERNET OF HEALTHCARE THINGS

The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making.

The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.

Audience

Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.

✦ Table of Contents

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
Section 1: Security and Privacy Concern in IoHT
1 Data Security and Privacy Concern in the Healthcare System
1.1 Introduction
1.2 Privacy and Security Concerns on E-Health Data
1.3 Levels of Threat to Information in Healthcare Organizations
1.4 Security and Privacy Requirement
1.5 Security of Healthcare Data
1.5.1 Existing Solutions
1.5.2 Future Challenges in Security and Privacy in the Healthcare Sector
1.5.3 Future Work to be Done in Security and Privacy in the Healthcare Sector
1.6 Privacy-Preserving Methods in Data
1.7 Conclusion
References
2 Authentication and Authorization Mechanisms for Internet of Healthcare Things
2.1 Introduction
2.2 Stakeholders in IoHT
2.3 IoHT Process Flow
2.4 Sources of Vulnerability
2.5 Security Features
2.6 Challenges to the Security Fabric
2.7 Security Techniques—User Authentication
2.8 Conclusions
References
3 Security and Privacy Issues Related to Big Data-Based Ubiquitous Healthcare Systems
3.1 Introduction
3.2 Big Data Privacy & Security Issues
3.3 Big Data Security Problem
3.3.1 Big Data Security Lifecycle
3.3.2 Threats & Attacks on Big Data
3.3.3 Current Technologies in Use
3.4 Privacy of Big Data in Healthcare
3.4.1 Data Protection Acts
3.5 Privacy Conserving Methods in Big Data
3.6 Conclusion
References
Section 2: Application of Machine Learning, Blockchain and Fog Computing on IoHT
4 Machine Learning Aspects for Trustworthy Internet of Healthcare Things
4.1 Introduction
4.2 Overview of Internet of Things
4.2.1 Application Area of IoT
4.3 Security Issues of IoT
4.3.1 Authentication
4.3.2 Integrity
4.3.3 Confidentiality
4.3.4 Non-Repudiation
4.3.5 Authorization
4.3.6 Availability
4.3.7 Forward Secrecy
4.3.8 Backward Secrecy
4.4 Internet of Healthcare Things (IoHT): Architecture and Challenges
4.4.1 IoHT Support
4.4.2 IoHT Architecture and Data Processing Stages
4.4.3 Benefits Associated With Healthcare Based on the IoT
4.4.4 Challenges Faced by IoHT
4.4.5 Needs in IoHT
4.5 Security Protocols in IoHT
4.5.1 Key Management
4.5.2 User/Device Authentication
4.5.3 Access Control/User Access Control
4.5.4 Intrusion Detection
4.6 Application of Machine Learning for Intrusion Detection in IoHT
4.7 Proposed Framework
4.8 Conclusion
References
5 Analyzing Recent Trends and Public Sentiment for Internet of Healthcare Things and Its Impact on Future Health Crisis
5.1 Introduction
5.2 Literature Review
5.3 Overview of the Internet of Healthcare Things
5.4 Performing Topic Modeling on IoHTs Dataset
5.5 Performing Sentiment Analysis on IoHTs Dataset
5.6 Conclusion and Future Scope
References
6 Rise of Telemedicine in Healthcare Systems Using Machine Learning: A Key Discussion
6.1 Introduction
6.2 Types of Machine Learning
6.3 Telemedicine Advantages
6.4 Telemedicine Disadvantages
6.5 Review of Literature
6.6 Fundamental Key Components Needed to Begin Telemedicine
6.6.1 Collaboration Instruments
6.6.2 Clinical Peripherals
6.6.3 Work Process
6.6.4 Cloud-Based Administrations
6.7 Types of Telemedicine
6.7.1 Store-and-Forward Method
6.7.2 Remote Monitoring
6.7.3 Interactive Services
6.8 Benefits of Telemedicine
6.9 Application of Telemedicine Using Machine Learning
6.10 Innovation Infrastructure of Telemedicine
6.11 Utilization of Mobile Wireless Devices in Telemedicine
6.12 Conclusion
References
7 Trusted Communication in the Healthcare Sector Using Blockchain
7.1 Introduction
7.2 Overview of Blockchain
7.3 Medical IoT Concerns
7.3.1 Security Concerns
7.3.2 Privacy Concerns
7.3.3 Trust Concerns
7.4 Needs for Security in Medical IoT
7.5 Uses of Blockchain in Healthcare
7.6 Solutions for IoT Healthcare Cyber-Security
7.6.1 Architecture of the Smart Healthcare System
7.7 Executions of Trusted Environment
7.7.1 Root of Trust Security Services
7.7.2 Chain of Trust Security Services
7.8 Patient Registration Using Medical IoT Devices
7.8.1 Encryption
7.8.2 Key Generation
7.8.3 Security by Isolation
7.8.4 Virtualization
7.9 Trusted Communications Using Blockchain
7.9.1 Record Creation Using IoT Gateways
7.9.2 Accessibility to Patient Medical History
7.9.3 Patient Enquiry With the Hospital Authority
7.9.4 Blockchain-Based IoT System Architecture
7.10 Combined Workflows
7.10.1 Layer 1: The Gateway Collects IoT Data and Generates a New Record
7.10.2 Layer 2: Gateway/Authority Want to Access Patient’s Medical Record
7.10.3 Layer 3: Patient Visits and Interact With an Authority
7.11 Conclusions
References
8 Blockchain in Smart Healthcare Management
8.1 Introduction
8.2 Healthcare Industry
8.2.1 Classification of Healthcare Services
8.2.2 Health Information Technology (HIT)
8.2.3 Issues and Challenges Faced by Major Stakeholders in the Healthcare Industry
8.3 Blockchain Technology
8.3.1 Important Terms
8.3.2 Features of Blockchain
8.3.3 Workings of a Blockchain System
8.3.4 Applications of Blockchain
8.3.5 Challenges and Drawbacks of Blockchain
8.4 Applications of Blockchain in Healthcare
8.4.1 Electronic Medical Records (EMR) and Electronic Health Records (EHR)
8.4.2 Management System
8.4.3 Remote Monitoring/IoMT
8.4.4 Insurance Industry
8.4.5 Drug Counterfeiting
8.4.6 Clinical Trials
8.4.7 Public Health Management
8.5 Challenges of Blockchain in Healthcare
8.6 Future Research Directions
8.7 Conclusion
References
Section 3: Case Studies of Healthcare
9 Organ Trafficking on the Dark Web—The Data Security and Privacy Concern in Healthcare Systems
9.1 Introduction
9.2 Inclination for Cybersecurity Web Peril
9.3 Literature Review
9.4 Market Paucity or Organ Donors
9.5 Organ Harvesting and Transplant Tourism Revenue
9.6 Social Web Net Crimes
9.7 DW—Frontier of Illicit Human Harvesting
9.8 Organ Harvesting Apprehension
9.9 Result and Discussions
9.10 Conclusions
References
10 Deep Learning Techniques for Data Analysis Prediction in the Prevention of Heart Attacks
10.1 Introduction
10.2 Literature Survey
10.3 Materials and Method
10.3.1 Cohort Study
10.4 Training Models
10.4.1 Artificial Neural Network (ANN)
10.4.2 K-Nearest Neighbor Classifier
10.4.3 Naïve Bayes Classifier
10.4.4 Decision Tree Classifier (DTC)
10.4.5 Random Forest Classifier (RFC)
10.4.6 Neural Network Implementation
10.5 Data Preparation
10.5.1 Multi-Layer Perceptron Neural Network (MLPNN) Algorithm and Prediction
10.6 Results Obtained
10.6.1 Accuracy
10.6.2 Data Analysis
10.7 Conclusion
References
11 Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)
11.1 Introduction
11.2 Related Work
11.3 IoT and Disease
11.4 Research Materials and Methods
11.4.1 Dataset
11.4.2 Data Pre-Processing
11.4.3 Classification Algorithms
11.5 Experimental Outcomes
11.6 Conclusion
References
12 Perspective-Based Studies of Trust in IoHT and Machine Learning-Brain Cancer
12.1 Introduction
12.2 Literature Survey
12.3 Illustration of Brain Cancer
12.3.1 Brain Tumor
12.3.2 Types of Brain Tumors
12.3.3 Grades of Brain Tumors
12.3.4 Symptoms of Brain Tumors
12.4 Sleuthing and Classification of Brain Tumors
12.4.1 Sleuthing of Brain Tumors
12.4.2 Challenges During Classification of Brain Tumors
12.5 Survival Rate of Brain Tumors
12.6 Conclusion
References
Index