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E-raamat: Internet of Healthcare Things: Machine Learning for Security and Privacy

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Internet of Healthcare Things: Machine Learning for Security and PrivacySuurem pilt
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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.
Preface xiii
Section 1: Security and Privacy Concern in IoHT 1(64)
1 Data Security and Privacy Concern in the Healthcare System
3(24)
Ahuja Sourav
1.1 Introduction
3(3)
1.2 Privacy and Security Concerns on E-Health Data
6(1)
1.3 Levels of Threat to Information in Healthcare Organizations
6(3)
1.4 Security and Privacy Requirement
9(2)
1.5 Security of Healthcare Data
11(7)
1.5.1 Existing Solutions
11(4)
1.5.2 Future Challenges in Security and Privacy in the Healthcare Sector
15(1)
1.5.3 Future Work to be Done in Security and Privacy in the Healthcare Sector
16(2)
1.6 Privacy-Preserving Methods in Data
18(4)
1.7 Conclusion
22(1)
References
23(4)
2 Authentication and Authorization Mechanisms for Internet of Healthcare Things
27(14)
Srinivasan Lakshmi Narasimhan
2.1 Introduction
28(1)
2.2 Stakeholders in IoHT
29(2)
2.3 IoHT Process Flow
31(2)
2.4 Sources of Vulnerability
33(1)
2.5 Security Features
34(1)
2.6 Challenges to the Security Fabric
35(1)
2.7 Security Techniques-User Authentication
36(1)
2.8 Conclusions
37(1)
References
38(3)
3 Security and Privacy Issues Related to Big Data-Based Ubiquitous Healthcare Systems
41(24)
Jaspreet Singh
3.1 Introduction
41(1)
3.2 Big Data Privacy & Security Issues
42(1)
3.3 Big Data Security Problem
43(7)
3.3.1 Big Data Security Lifecycle
44(3)
3.3.2 Threats & Attacks on Big Data
47(1)
3.3.3 Current Technologies in Use
48(2)
3.4 Privacy of Big Data in Healthcare
50(6)
3.4.1 Data Protection Acts
50(1)
3.4.1.1 HIPAA Compliance
50(1)
3.4.1.2 HIPAA Five Rules
53(3)
3.5 Privacy Conserving Methods in Big Data
56(4)
3.6 Conclusion
60(1)
References
61(4)
Section 2: Application of Machine Learning, Blockchain and Fog Computing on IoHT 65(124)
4 Machine Learning Aspects for Trustworthy Internet of Healthcare Things
67(28)
Pradeep Bedi
S.B. Goyal
Jugnesh Kumar
Preetishree Patnaik
4.1 Introduction
68(1)
4.2 Overview of Internet of Things
69(5)
4.2.1 Application Area of IoT
72(1)
4.2.1.1 Wearable Devices
73(1)
4.2.1.2 Smart Home Applications
73(1)
4.2.1.3 Healthcare IoT Applications
73(1)
4.2.1.4 Smart Cities
73(1)
4.2.1.5 Smart Agriculture
74(1)
4.2.1.6 Industrial Internet of Things
74(1)
4.3 Security Issues of IoT
74(2)
4.3.1 Authentication
75(1)
4.3.2 Integrity
75(1)
4.3.3 Confidentiality
75(1)
4.3.4 Non-Repudiation
75(1)
4.3.5 Authorization
76(1)
4.3.6 Availability
76(1)
4.3.7 Forward Secrecy
76(1)
4.3.8 Backward Secrecy
76(1)
4.4 Internet of Healthcare Things (IoHT): Architecture and Challenges
76(6)
4.4.1 IoHT Support
77(1)
4.4.2 IoHT Architecture and Data Processing Stages
78(2)
4.4.3 Benefits Associated With Healthcare Based on the IoT
80(1)
4.4.4 Challenges Faced by IoHT
81(1)
4.4.5 Needs in IoHT
81(1)
4.5 Security Protocols in IoHT
82(2)
4.5.1 Key Management
83(1)
4.5.2 User/Device Authentication
83(1)
4.5.3 Access Control/User Access Control
83(1)
4.5.4 Intrusion Detection
83(1)
4.6 Application of Machine Learning for Intrusion Detection in IoHT
84(2)
4.7 Proposed Framework
86(4)
4.8 Conclusion
90(1)
References
90(5)
5 Analyzing Recent Trends and Public Sentiment for Internet of Healthcare Things and Its Impact on Future Health Crisis
95(18)
Upendra Dwivedi
5.1 Introduction
96(1)
5.2 Literature Review
97(3)
5.3 Overview of the Internet of Healthcare Things
100(4)
5.4 Performing Topic Modeling on IoHTs Dataset
104(3)
5.5 Performing Sentiment Analysis on IoHTs Dataset
107(3)
5.6 Conclusion and Future Scope
110(1)
References
111(2)
6 Rise of Telemedicine in Healthcare Systems Using Machine Learning: A Key Discussion
113(18)
Shaweta Sachdeva
Aleem Ali
6.1 Introduction
114(1)
6.2 Types of Machine Learning
115(1)
6.3 Telemedicine Advantages
115(1)
6.4 Telemedicine Disadvantages
116(1)
6.5 Review of Literature
116(2)
6.6 Fundamental Key Components Needed to Begin Telemedicine
118(1)
6.6.1 Collaboration Instruments
118(1)
6.6.2 Clinical Peripherals
119(1)
6.6.3 Work Process
119(1)
6.6.4 Cloud-Based Administrations
119(1)
6.7 Types of Telemedicine
119(5)
6.7.1 Store-and-Forward Method
119(1)
6.7.1.1 Telecardiology
120(1)
6.7.1.2 Teleradiology
121(1)
6.7.1.3 Telepsychiatry
121(1)
6.7.1.4 Telepharmacy
121(2)
6.7.2 Remote Monitoring
123(1)
6.7.3 Interactive Services
123(1)
6.8 Benefits of Telemedicine
124(1)
6.9 Application of Telemedicine Using Machine Learning
125(1)
6.10 Innovation Infrastructure of Telemedicine
125(1)
6.11 Utilization of Mobile Wireless Devices in Telemedicine
126(1)
6.12 Conclusion
127(1)
References
128(3)
7 Trusted Communication in the Healthcare Sector Using Blockchain
131(30)
Balasamy K.
7.1 Introduction
131(2)
7.2 Overview of Blockchain
133(1)
7.3 Medical IoT Concerns
134(1)
7.3.1 Security Concerns
134(1)
7.3.2 Privacy Concerns
135(1)
7.3.3 Trust Concerns
135(1)
7.4 Needs for Security in Medical IoT
135(2)
7.5 Uses of Blockchain in Healthcare
137(1)
7.6 Solutions for IoT Healthcare Cyber-Security
138(2)
7.6.1 Architecture of the Smart Healthcare System
139(1)
7.6.1.1 Data Perception Layer
139(1)
7.6.1.2 Data Communication Layer
140(1)
7.6.1.3 Data Storage Layer
140(1)
7.6.1.4 Data Application Layer
140(1)
7.7 Executions of Trusted Environment
140(4)
7.7.1 Root of Trust Security Services
141(2)
7.7.2 Chain of Trust Security Services
143(1)
7.8 Patient Registration Using Medical IoT Devices
144(5)
7.8.1 Encryption
145(1)
7.8.2 Key Generation
146(1)
7.8.3 Security by Isolation
146(1)
7.8.4 Virtualization
146(3)
7.9 Trusted Communications Using Blockchain
149(3)
7.9.1 Record Creation Using IoT Gateways
150(1)
7.9.2 Accessibility to Patient Medical History
151(1)
7.9.3 Patient Enquiry With the Hospital Authority
151(1)
7.9.4 Blockchain-Based IoT System Architecture
151(1)
7.9.4.1 First Layer
151(1)
7.9.4.2 Second Layer
152(1)
7.9.4.3 Third Layer
152(1)
7.10 Combined Workflows
152(2)
7.10.1 Layer 1: The Gateway Collects IoT Data and Generates a New Record
152(1)
7.10.2 Layer 2: Gateway/Authority Want to Access Patient's Medical Record
153(1)
7.10.3 Layer 3: Patient Visits and Interact With an Authority
153(1)
7.11 Conclusions
154(1)
References
154(7)
8 Blockchain in Smart Healthcare Management
161(28)
Jayant Barak
Harshwardhan Chaudhary
Rakshit Mangal
Aarti Goel
Deepak Kumar Sharma
8.1 Introduction
162(1)
8.2 Healthcare Industry
163(5)
8.2.1 Classification of Healthcare Services
163(1)
8.2.2 Health Information Technology (HIT)
164(1)
8.2.3 Issues and Challenges Faced by Major Stakeholders in the Healthcare Industry
165(1)
8.2.3.1 The Patient
166(1)
8.2.3.2 The Pharmaceutical Industry
166(1)
8.2.3.3 The Healthcare Service Providers
166(1)
8.2.3.4 The Government
167(1)
8.2.3.5 Insurance Company
167(1)
8.3 Blockchain Technology
168(8)
8.3.1 Important Terms
168(2)
8.3.2 Features of Blockchain
170(1)
8.3.2.1 Decentralization
170(1)
8.3.2.2 Immutability
170(1)
8.3.2.3 Transparency
171(1)
8.3.2.4 Smart Contracts
171(1)
8.3.3 Workings of a Blockchain System
171(2)
8.3.4 Applications of Blockchain
173(1)
8.3.4.1 Financial Services
173(1)
8.3.4.2 Healthcare
173(1)
8.3.4.3 Supply Chain
173(1)
8.3.4.4 Identity Management
173(1)
8.3.4.5 Voting
173(1)
8.3.5 Challenges and Drawbacks of Blockchain
174(2)
8.4 Applications of Blockchain in Healthcare
176(7)
8.4.1 Electronic Medical Records (EMR) and Electronic Health Records (EHR)
176(1)
8.4.2 Management System
177(1)
8.4.3 Remote Monitoring/IoMT
178(1)
8.4.4 Insurance Industry
179(1)
8.4.5 Drug Counterfeiting
180(2)
8.4.6 Clinical Trials
182(1)
8.4.7 Public Health Management
182(1)
8.5 Challenges of Blockchain in Healthcare
183(1)
8.6 Future Research Directions
184(1)
8.7 Conclusion
185(1)
References
186(3)
Section 3: Case Studies of Healthcare 189(92)
9 Organ Trafficking on the Dark Web-The Data Security and Privacy Concern in Healthcare Systems
191(26)
Romil Rawat
Bhagwati Garg
Vinod Mahor
Shrikant Telang
Kiran Pachlasiya
Mukesh Chouhan
9.1 Introduction
192(2)
9.2 Inclination for Cybersecurity Web Peril
194(3)
9.3 Literature Review
197(2)
9.4 Market Paucity or Organ Donors
199(4)
9.5 Organ Harvesting and Transplant Tourism Revenue
203(1)
9.6 Social Web Net Crimes
204(5)
9.7 DW-Frontier of Illicit Human Harvesting
209(1)
9.8 Organ Harvesting Apprehension
209(3)
9.9 Result and Discussions
212(1)
9.10 Conclusions
212(1)
References
213(4)
10 Deep Learning Techniques for Data Analysis Prediction in the Prevention of Heart Attacks
217(24)
C.V. Aravinda
Meng Lin
Udaya Kumar
Reddy K.R.
G. Amar Prabhu
Abbreviations
218(1)
10.1 Introduction
218(1)
10.2 Literature Survey
219(2)
10.3 Materials and Method
221(1)
10.3.1 Cohort Study
222(1)
10.4 Training Models
222(5)
10.4.1 Artificial Neural Network (ANN)
222(2)
10.4.2 K-Nearest Neighbor Classifier
224(1)
10.4.3 Naive Bayes Classifier
225(1)
10.4.4 Decision Tree Classifier (DTC)
226(1)
10.4.5 Random Forest Classifier (RFC)
226(1)
10.4.6 Neural Network Implementation
226(1)
10.5 Data Preparation
227(1)
10.5.1 Multi-Layer Perceptron Neural Network (MLPNN) Algorithm and Prediction
227(1)
10.6 Results Obtained
228(8)
10.6.1 Accuracy
228(1)
10.6.2 Data Analysis
228(8)
10.7 Conclusion
236(1)
References
236(5)
11 Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)
241(24)
Monika Lamba
Geetika Munjal
Yogita Gigras
11.1 Introduction
242(3)
11.2 Related Work
245(5)
11.3 IoT and Disease
250(1)
11.4 Research Materials and Methods
251(2)
11.4.1 Dataset
251(1)
11.4.2 Data Pre-Processing
252(1)
11.4.3 Classification Algorithms
252(1)
11.5 Experimental Outcomes
253(4)
11.6 Conclusion
257(1)
References
258(7)
12 Perspective-Based Studies of Trust in IoHT and Machine Learning-Brain Cancer
265(16)
Sweta Kumari
Akhilesh Kumar Sharma
Sandeep Chaurasia
Shamik Tiwari
12.1 Introduction
266(1)
12.2 Literature Survey
267(1)
12.3 Illustration of Brain Cancer
268(5)
12.3.1 Brain Tumor
268(1)
12.3.2 Types of Brain Tumors
269(1)
12.3.3 Grades of Brain Tumors
270(1)
12.3.4 Symptoms of Brain Tumors
271(2)
12.4 Sleuthing and Classification of Brain Tumors
273(1)
12.4.1 Sleuthing of Brain Tumors
273(1)
12.4.2 Challenges During Classification of Brain Tumors
274(1)
12.5 Survival Rate of Brain Tumors
274(4)
12.6 Conclusion
278(1)
References
279(2)
Index 281
Kavita Sharma, PhD is an associate professor in the Department of CSE at Galgotias College of Engineering and Technology, Greater Noida, India. She has 4 patents (2 Granted and 2 published), published 6 books and 50 research articles in international journals and conferences. Her area of interest includes information and cyber security, mobile computing, IoT security, data analytics and machine learning.

Yogita Gigras, PhD is an assistant professor in the Department of CSE & IT, School of Engineering & Technology of The North Cap University, Haryana, India. She has published more than 30 research papers in peer-reviewed international journals and conferences and has more than 12 years of teaching experience at both post and undergraduate level.

Vishnu Sharma, PhD is Head of Department and Professor in Computer Science and Engineering at Galgotias College of Engineering and Technology Greater Noida, UP, India. He has published more than 50 research papers in international and national journals and conferences as well as two books on mobile computing. He has more than 21 years of teaching experience in engineering institutes and universities.

D. Jude Hemanth, PhD is at the Department of ECE, Karunya University, Coimbatore, India. He has authored more than 100 research papers in SCIE/Scopus indexed international journals conferences as well as authored 1 book and edited 11 others.

Ramesh Chandra (Poonia), PhD is an associate professor in the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India. He has authored more than 65 research papers in SCIE/Scopus indexed international journals conferences as well as authored 6 books.
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