Intelligent Data Security Solutions for e-Health Applications 9780128195116, 0128195118

Table of contents :
Front-Matter_2020_Intelligent-Data-Security-Solutions-for-e-Health-Applicati
Front Matter
Copyright_2020_Intelligent-Data-Security-Solutions-for-e-Health-Applications
Copyright
Contributors_2020_Intelligent-Data-Security-Solutions-for-e-Health-Applicati
Contributors
Preface_2020_Intelligent-Data-Security-Solutions-for-e-Health-Applications
Preface
Outline of the book and chapter synopsis
Special Acknowledgments
Chapter-1---Perceptual-hashing-based-nov_2020_Intelligent-Data-Security-Solu
Perceptual hashing-based novel security framework for medical images
Introduction
Mathematical preliminaries
SIFT features
Nonlinear chaotic map
Singular value decomposition
Discrete cosine transform
Proposed technique
Perceptual feature extraction
Hash generation process
Watermark construction
Watermark verification
Experimental results and discussion
Robustness analysis
Key sensitivity analysis
Computational time complexity
Conclusion
References
Chapter-2---Frequency-domain-based-data_2020_Intelligent-Data-Security-Solut
Frequency domain based data hiding for encrypted medical images
Introduction
Literature survey
Theoretical background
Histogram shifting RDH method
Integer wavelet transform with lifting scheme
The proposed algorithm
Watermark embedding procedure
Phase 1-Image preprocessing
Phase 2-Image segmentation
Phase 3-Frequency domain payload generation phase
Phase 4-Data embedding phase
Watermark extraction procedure
Performance evaluation
Test images
Performance evaluation metrics
Embedding capacity
Image visual quality
Entropy
Performance of the proposed algorithm in the frequency domain
Performance of the proposed algorithm in the encrypted domain
Combined performance of the frequency and encrypted domains
Pure embedding capacity
Comparison of the proposed algorithms against state-of-the-art studies
Conclusions and future work
References
Chapter-3---An-OpenSim-guided-tour-in-ma_2020_Intelligent-Data-Security-Solu
An OpenSim guided tour in machine learning for e-health applications
Introduction
State of the art
Basic musculoskeletal elements and capabilities of OpenSim
OpenSim capabilities
Applications of OpenSim
OpenSim: Musculoskeletal simulation framework
The OpenSim model
Importing experimental data
Scaling
The inverse problem
The forward problem
Analyzing simulations
Methodology
OpenSim: Plugins, research issues, and future trends
Plugins
Research issues and future trends
References
Further reading
Chapter-4---Advances-and-challenges-_2020_Intelligent-Data-Security-Solution
Advances and challenges in fMRI and DTI techniques
Introduction
fMRI analysis and survey
Application
DTI analysis and survey
Application
Fusion analysis of fMRI and DTI
Applications
Classification and prediction methods and scope
Traditional classifiers
Deep learning classifiers
Future directions and challenges
Challenges
Trends and future directions
Conclusions and important findings
References
Chapter-5---Homomorphic-transform-based-dual-i_2020_Intelligent-Data-Securit
Homomorphic transform-based dual image watermarking using IWT-SVD for secure e-healthcare applications
Introduction
Significant features of the proposed technique
Basic terminologies
Homomorphic transform
Integer wavelet transform
Singular value decomposition
Arnold transform
Proposed watermarking technique
Embedding process
Extraction process
Simulation results
Conclusions
References
Chapter-6---An-analysis-of-security-acces_2020_Intelligent-Data-Security-Sol
An analysis of security access control on healthcare records in the cloud
Introduction
Review of the EHR literature
Overview of electronic health records
Important components
Electronic medical records
Health information exchange (HIE)
Threat model of EHR
Healthcare access-control requirements
Access-control requirements
Access-control mechanisms for EHR
Access-control policy specification for EHRs
Security requirements
Categories of ACMs
Discretionary access control (DAC) for EHR
Mandatory access control (MAC) for EHR
Biba model
Role-based access control (RBAC) for EHR
Benefits of RABC
Attribute-based access control (ABAC) for EHR
Access-control constraints for EHRs
Overall performance of access controls
Conclusions
References
Chapter-7---Security-and-interference-manage_2020_Intelligent-Data-Security-
Security and interference management in the cognitive-inspired Internet of Medical Things
Introduction
Constituents of the cognitive-inspired Internet of Medical Things
Spectrum sharing in cognitive radio networks
Internet of Things
Internet of Medical Things
Cognitive-inspired Internet of Medical Things
Spectrum sensing techniques
Energy-based spectrum sensing
Matched filter detection
Feature detection
Eigenvalue-based detector
Spectrum accessing techniques
Interweave spectrum accessing
Underlay spectrum accessing
Overlay spectrum accessing
Hybrid spectrum accessing technique
Interference management in the cognitive-inspired Internet of Medical Things
Spectrum sensing
Spectrum prediction
Transmission below the PU interference tolerable limit
Using advanced encoding techniques
Spectrum monitoring
Security concerns regarding the cognitive-inspired IoMT
Conclusion
References
Chapter-8---Access-control-and-classifier-ba_2020_Intelligent-Data-Security-
Access control and classifier-based blockchain technology in e-healthcare applications
Introduction
Related works
Purpose of BT
Methodology for security
BT-A distributed ledger technology
Classifier: An SVM
Pros of the proposed SVM
RBF-SVM classifier
RBF
E-healthcare security analysis via BT
Procedure of BTs
Important elements in BT
BT toward security
Access-control model for e-healthcare
Result analysis
Conclusion
Acknowledgment
References
Chapter-9---Machine-learning-algorith_2020_Intelligent-Data-Security-Solutio
Machine learning algorithms for medical image security
Introduction
Deep learning for steganography
Brief insight into deep learning networks
Least significant bit substitution using a feed-forward neural network
Deep-stego
Steganography using deep convolutional generative adversarial networks
CNN-based adversarial embedding
Machine learning for steganalysis
Steganalysis using CNNs
Support vector machine-based steganalyzer for LSB matching steganography
Machine learning for medical image encryption
Iris image encryption using CNN
Combined encryption and data hiding using SVMs
Machine learning for privacy in medical images
CNN for homomorphic inference on encrypted medical images
Random forest for privacy preserving and disease prediction
Conclusion
References
Chapter-10---Genetic-algorithm-based-intelligen_2020_Intelligent-Data-Securi
Genetic algorithm-based intelligent watermarking for security of medical images in telemedicine applications
Introduction
Genetic algorithm-based image watermarking
Technical background
Image transformation
Genetic algorithm
Proposed scheme
Embedding process
Extraction process
Selection of proper scaling factor using GA
Results and discussion
Imperceptibility test
Robustness test
Performance comparison
Conclusions
References
Chapter-11---Data-security-for-WBAN-_2020_Intelligent-Data-Security-Solution
Data security for WBAN in e-health IoT applications
Introduction
E-health applications
WBAN technology
WBAN architecture
Security challenge in WBAN
Security attacks in WBAN
Attacks at the data collection level
Attacks at transmission level
Data security advancements
Survey on encryption algorithms
Survey on authentication algorithms
Conclusion
References
Chapter-12---Cloud-based-computer-assis_2020_Intelligent-Data-Security-Solut
Cloud-based computer-assisted diagnostic solutions for e-health
Introduction
Enabling techniques for IoT-based early diagnostic systems
Digital signal/image processing
Artificial intelligence/machine learning/deep learning
Medical sensor based
Internet of Medical Things
IoT hardware design
Cloud-based intelligent diagnostic system
Cloud-based early diagnostic systems
Cataract
Diabetic retinopathy/glaucoma [21]
M-cardiac care platform
Risk of fall detection
Challenges in cloud-based e-health systems
Chapter summary
References
Chapter-13---Progressive-advancements-in-sec_2020_Intelligent-Data-Security-
Progressive advancements in security challenges, issues, and solutions in e-health systems
Introduction to e-health systems
Telehomecare
Telerehabilitation
Remote physiological monitoring
Telenursing
Remote patient monitoring
Telehealthcare
Teleconsultation
Applications of telemedicine
Telestroke
Telemedicine in the management of gestational diabetes management (GDM)
Telemedicine in diabetes retinopathy
Telemedicine in surgery or telesurgery
Telemedicine in the management of chronic liver disease
Telemedicine for finding nucleosome positioning
Telemedicine in postsurgical care
Security attacks and solutions
Attacks at the data collection level
Jamming attack
Data collision attack
Desynchronization attack
Spoofing attack
Selective forwarding attack
Sybil attacks
Attacks at the transmission level
Man-in-the-middle attack
Data tampering attack
Scrambling attack
Signaling attack
Unfairness in allocation
Message modification attack
Hello flood attack
Data interception
Wormhole attack
Attacks at the storage level
Inference of patients information
Malware attack
Social engineering attacks
Removable distribution media attack
Security challenges and issues in telemedicine
Security solutions
Limitations of telemedicine
Role of IoT and cloud in telemedicine
Future of telemedicine
Disease heterogeneity
Precision medicine
Drug safety
Decentralized care system
Patient-centric medical homes will become a reality
Assistive technologies will become cheaper
Wearable, implantable, and microcapsule devices
Smart-based healthcare network
Conclusion
References
Chapter-14---Despeckling-of-ultrasound-images-_2020_Intelligent-Data-Securit
Despeckling of ultrasound images based on the multiresolution approach and Gaussianization transform
Introduction
Background and basic principles
Discrete wavelet transform
Distribution of wavelet coefficients and their statistical modeling
Goodness-of-fit analysis
Gaussianization transformation
Bayesian MMSE estimator
Methodology
Simulation results
Conclusion and discussion
References
Chapter-15---Wireless-medical-sensor-_2020_Intelligent-Data-Security-Solutio
Wireless medical sensor networks for smart e-healthcare
Introduction
Typical medical body sensors in a WSN
Different scenarios in WSN-based e-healthcare
Framework for WSN enabled e-healthcare
Real-time application of WSN networks in e-healthcare
WSN applications for cardiovascular diseases
WSN applications for the care of children and people of an elderly age
WSN applications for Alzheimers disease and other mental illnesses
MAC layer protocol design for e-health applications
Contention-based MAC protocols
Schedule-based MAC protocols
Hybrid MAC protocols
Challenges and research issues for WSN-based healthcare
Conclusions
References
Chapter-16---A-secure-lightweight-mutual-auth_2020_Intelligent-Data-Security
A secure lightweight mutual authentication and key agreement protocol for healthcare systems
Introduction
Organization of the chapter
Essential building blocks of the proposed protocol
Biometric fuzzy extractor function
Bitwise X-OR function
Literature review
System model
Network model
Threat model
Proposed security scheme
Set-up phase
Mobile registration
Log-in phase
Authentication and key agreement phase
Password update phase
Analysis of proposed work
Security analysis using AVISPA
Security proof using Burrows-Adabi-Needham logic
Computation cost estimation and comparison with other works
Conclusion
References
Index_2020_Intelligent-Data-Security-Solutions-for-e-Health-Applications
Index
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