NAGARJUNA COLLEGE OF ENGINEERING & TECHNOLOGY,
DEVANAHALLI, BENGALURU – 562164.
(An Autonomous college under VTU, Accredited by NAAC with “A+” Grade)
PRESENTATION
ON
Fortified-Chain 2.0: Intelligent Blockchain for Decentralized
Smart Healthcare System
Submitted by
1NC20CS075
Mr. Vinay R G
Under the guidance of
Prof Raghavendra B
Assistant Professor Dept. of CSE, NCET.
CONTENTS
1.
About
2.
Introduction
3.
Architecture Of Fortified Chain 2.0
4.
Workflow
5.
Algorithms And Mechanisms
6.
Experimental Results
7.
Conclusions
8.
References
ABOUT
• Proposed is a decentralized smart healthcare system called Fortified-Chain 2.0.
• These devices currently face issues like single points of failure (SPoF)and loss of user control over medical
data due to reliance on centralized systems.
• Fortified-Chain 2.0 utilizes blockchain technology.
• Additionally, it introduces a hybrid computing approach
• Fortified-Chain 2.0 operates on a private blockchain-based Distributed Data Storage System (DDSS)
INTRODUCTION
1.
Issue with Centralized Systems
2.
Decentralized Solutions
3.
Blockchain and Edge Computing
4.
Security and Privacy
5.
File Sharing and Data Analysis
6.
Community-Based Control
ARCHITECTURE OF FORTIFIED CHAIN 2.0
• Preliminaries
Hyperledger Fabric and Chaincode
Distributed Decentralised Storage System
Hybrid Computing
Modelling Techniques and Machine Learning
• Architecture
• System Architecture Layers
Data Layer
AI/ML Layer
Alert/Automation Layer
PRELIMINARIES
Hyperledger Fabric and Chaincode:
• Certificate Authority (CA)
• Client
• Peer Nodes:
• Endorsing Peer Nodes: Execute chaincode, endorse transactions.
• Committing Peer Nodes: Receive endorsed transactions, validate and commit blocks to the ledger.
• Orderer Node
• Distributed Decentralized Storage System (DDSS):
• IPFS (Inter Planetary File System):
• Decentralized file storage system.
• Files stored across multiple network nodes, eliminating reliance on central servers.
• Uses unique addressing based on file content to eliminate redundancy.
• Hyperledger:
• Manages transactions and permissions.
• Ensures only authorized users access and share files.
• Tracks file ownership.
• Distributed Hash Table (DHT):
• Acts as a directory for file location in the network.
• Devices check DHT to find hosting nodes of desired files.
• Hybrid Computing :
• Cloud Computing
• Edge Computing
• Modelling Techniques and Machine Learning
• RF : In machine learning, both RF and SVM are supervised learning algorithms, where RF is an ensemble bagging type of
machine learning algorithm.
• SVM : Given, a set of training examples, the SVM classifier maps the training samples into a hyperspace so that it
maximizes the separation boundary between two or more categories. During the test, new samples are mapped into that
same space and predicted to a category based on which side of the gap they fall.
Architecture
• Logical Layers Client
• Access Control
• Isolation
• Node Selection
System Architecture Layers
• Data Layer: The data layer consists of :
• Sensors (data generators),
• Edge computing nodes (used to process data), and
• Storage network (DDSS), respectively.
• AI/ML Layer : It is logically separated into two sub-layers known as
• Hospital internal layer for in-patient services and
• Hospital external layer for remote patient services.
• Alert/Automation: The alert/automation layer of Fortified-Chain 2.0 is the bottom layer, where alerts are produced
using the information from the intelligence layer.
WORKFLOW
• Sensor Data Processing
• Patient Enrollment
• Data Processing and Analysis
• Alert Generation and Transmission
• Data Logging
• Remote Services
• Machine Learning Prediction
ALGORITHMS AND MECHANISMS
• The proposed security mechanism consists of mutual authentication and a selective ring-based access
control system.
STEP 1:Edge computer receives requests from sensors and actuators, and generates different node identities.
STEP 2:During the node registration process (2), each node shares their node type (Ntype) and public key (PubN ) with the
edge computer for further communication.
STEP 3:Simultaneously, the edge computer acknowledges the communication and generate registration identity (RID) for
each node.
STEP 4:Edge computer computes and stores H(RID) and RID in its cache, and concurrently generates the group identity
(GID) as well as accumulates in local database.
STEP 5:The registration process concludes with the dispatch of RID to the requester node.
STEP 6:The edge computer generates a mutual authentication token (MATN ) for each registered node.
STEP 7:The edge computer transfers the SID and MATN to the respective node in an encrypted format with the help of the
node’s public key.
• PROPOSED ML ALGORITHM :
• Fortified-Chain 2.0 Decentralized RFSVM Module : This module combines two machine learning algorithms,
Random Forest (RF) and Support Vector Machine (SVM), to automate primary health tasks.
• Deployment and Benefits: This model helps healthcare providers serve more patients efficiently by accurately
classifying primary diseases using machine learning.
EXPERIMENTAL RESULTS
• The setup consists of a platform with five virtual systems
• Two systems with 4GB RAM dedicated to edge computing.
• One system with 8GB RAM and 50GB storage for the cloud node.
• Two systems with 4GB RAM each for organizational or user personal level device.
CONCLUSION
• Our new system improves security and privacy in decentralized healthcare by using Secure Shared Memory
(SSM).
• It combines both cloud and local computing to reduce delays and single points of failure. We use a special
access control system and mutual authentication to protect data ownership and privacy.
• The proposed system outperformed the existing decentralized Fortified-Chain with an approximated value of
17% and six times when compared with the classical cloud-centric H-CPS model in overall system
throughput. In the scalability test it is consistently performing 10% improvement over its precede FortifiedChain.
References
[1] M. A Akka¸s, R SOKULLU, and H E Çetin. Healthcare and Patient Monitoring Using IoT. Internet
of Things, 11:100–173, 2020.
[2] S. Wang, J. Wang, X. Wang, T. Qiu, Y. Yuan, L. Ouyang, Y. Guo, and F. Wang. Blockchain-powered
parallel healthcare systems based on the acp approach. IEEE Transactions on Computational Social
Systems, 5:942– 950, 2018.
[3] A. F. Subahi. Edge-Based IoT Medical Record System: Requirements, Recommendations and
Conceptual Design. IEEE Access, 7:94150–94159, 2019.
[4] J Xu, K Xue, S Li, H Tian, J Hong, P Hong, and N Yu. Healthchain: A Blockchain-Based Privacy
Preserving Scheme for Large-Scale Health Data. IEEE Internet of Things Journal, 6(5):8770–8781,
2019.
[5] A. Awad Abdellatif, L. Samara, A. Mohamed, A. Erbad, C. Fabiana Chiasserini, M. Guizani, M.
Dennis O’Connor, and J Laughton. MEdge-Chain: Leveraging Edge Computing and Blockchain for
Efficient Medical Data Exchange. IEEE Internet of Things Journal, 8(21):15762–15775, 2021.