Intelligent Data Security Solutions for e-Health Applications

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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