Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application
Round 1
Reviewer 1 Report
The author needs to highlight the risk of lightweight cryptography.
The author needs to highlight how the importance of nodes to provide a trusted, secure, transparent, and efficient environment for accessing and managing electronic health records
The author need to highlight proper research design by including sample size,unit analysis etc
The author need to present policy implication in the paper as well
Author Response
- The author needs to highlight the risk of lightweight cryptography.
Response: Thanks very much for your comments. We have provided more discussions on risk of lightweight cryptography in Introduction section as follows,
Updates in Introduction section:
Even though the lightweight cryptography techniques are designed targeting the resource constrained IoT devices to work with minimal energy and resources in a faster manner but the major concern of lightweight cryptography is minimal security \cite{mouha2015design} and the initial mathematical computations make it more complex hence the implementation will be difficult. Considering blockchain as a powerful tool for securing sensitive medical image data, a blockchain based zero trust security model is designed to ensure the encrypted medical data is accessed only by the authenticated user in the network \cite{sultana2020towards}.
- The author needs to highlight how the importance of nodes to provide a trusted, secure, transparent, and efficient environment for accessing and managing electronic health records.
Response: Thanks very much for your comments. We have provided more discussions on the importance of trusted secure environment in motivation section as follows:
Updates in Motivation section:
In a distributed decentralized ledger based peer to peer network, the block generation will happen based on the consent of all the peers. It is challenging to achieve the consensus in a trust less distributed ecosystem where all the peers connected to the network will participate in block validation process, however trustfulness in validation is attained using consensus algorithms, as an agreement to gain the mutual trust among the peers without using any trusted third party authority. The consensus algorithm of the blockchain technology will affect the overall performance interns of throughput, delay, and fault-tolerance of the blockchain network.
- The author need to highlight proper research design by including sample size, unit analysis etc.
Response: Thanks very much for your comments. We have updated the simulation parameters in Experimental Results section as
Updates in Experimental Results section:
The proposed secure PBFT consensus algorithm was tested in a simulated environment, implemented using a python programming language in an Ubuntu 16.04 operating system with configuration Intel Core i7 2.2GHz CPU with 16GB RAM and 1TB hard disk. Simulation is carried in a local machine by considering maximum of 35 nodes, initial global reputation of all the nodes as 1/nodes and numerical data (assumed as health-data related to a patient) required for the experiment purpose are generated manually and are fed into the system as transactions for analyzing the proposed algorithm to measure the security, reliability and performance aspects considering parameters such as transaction throughput, latency, faulty node ratio, and communication between the nodes.
- The author need to present policy implication in the paper as well
Response: Thanks very much for your comments. We have provided more discussions on We have provided more discussions and analysis on the simulation results in Experimental Results section as
Updates in Experimental Results section:
Throughput Analysis
Figure.\ref{fig:fig6} shows the relationship between time and throughput in the traditional PBFT algorithm and proposed algorithm, the average throughput gap increases with respect to the time. Figure.\ref{fig:fig7} illustrates the changes of throughput with node transaction commit rate, irrespective of the request rate throughput rate of the proposed algorithm rises steadily compare to the PBFT. During the simulation after 1250 transactions, there is a steady increases transaction commit rate, this will be to minimal number of communication during the transaction validation process in proposed algorithm, hence time required for transaction confirmation is less compared to PBFT.
Transaction Latency
Figure. \ref{fig:fig8} shows the relationship between latency and the number of nodes, the latency of the proposed algorithm is less than the Traditional PBFT algorithm, indicating that nodes’ consensus speed is faster than PBFT algorithm. The main reason for achieving the minimal latency is grouping the nodes into consensus node list and backup node list , hence the number of nodes participating in the consensus process is minimal. During the simulations the number of nodes are varied from 2,4,6,8,... 30. As the number of nodes in the network increases the transaction latency will also in both PBFT and Proposed algorithm this is due to rise in internal communication required during verification. We also tested the proposed algorithm by considering the effect of block size on latency by considering the varied number of transactions on different blocks. Since the number of transactions is more in a block it takes a longer time to propagate and verification leads to higher latency.
Communication between nodes
The number of communications between the nodes to achieve consensus affects the overall performance of the system, which is interrelated to the number of resources consumed for the deployment of the algorithm. However, communication times are experimentally computed considering different nodes and compared with the traditional PBFT algorithm. Figure.\ref{fig:fig10} illustrates the communication times of PBFT and the proposed algorithm, the general observation is that the proposed model induces less communication overhead. The main reason for minimal communication between the nodes in proposed algorithm is due to the segregation nodes into two lists. However only a trusted consensus nodes will participate in transaction validation process. But, eventually if we vary the nodes from 2, 4, 6, .... 18 the number of communication will also increases. Finally, the proposed model consumes a less number of resources with minimal communication among the nodes.
To summarize, the experimental results of the proposed PBFT consensus algorithm has shown 25% increase in transaction confirmation time, steady increase in transaction latency as the number of nodes increases in the network with minimal communication overhead compared to the Traditional PBFT consensus algorithm. The comparative analysis of the proposed algorithm with the existing popular consensus algorithms by considering the parameters such as Nodes Participating in the consensus Process, Node Reliability Assessment, Network Type, Node Election Fairness, Communication Overhead, is shown in Table 3
Reviewer 2 Report
This paper introduces a secure Practical Byzantine Fault Tolerance (PBFT) consensus-based lightweight blockchain algorithm for Healthcare applications, to strengthen the PBFT consensus by allowing highly trusted nodes to participate in the consensus algorithm using Eigen Trust model and verifiable random function (VRF) to select a random primary node from a group of trusted consensus nodes.
Emergence and Working of blockchain is not properly explained. The following works can be helpful. Untangling blockchain technology: A survey on state of the art, security threats, privacy services, applications and future research directions; Blockchain for smart cities: A review of architectures, integration trends and future research directions.
Introduction section looks lengthy. Authors must divide the introduction into separate paragraphs highlighting the background, motivation, major contribution and paper organization.
Section 2 can be summarized in the form of a table. Authors must highlight and compare the contribution of various previous works that have been discussed.
The proposed methodology is not properly presented in the manuscript.
The considered simulation parameters must be presented before results and discussion.
Authors fail to compare the proposed scheme with existing techniques. Why is the proposed scheme superior ? This is not justified.
Author Response
Reviewer: 2
This paper introduces a secure Practical Byzantine Fault Tolerance (PBFT) consensus-based lightweight blockchain algorithm for Healthcare applications, to strengthen the PBFT consensus by allowing highly trusted nodes to participate in the consensus algorithm using Eigen Trust model and verifiable random function (VRF) to select a random primary node from a group of trusted consensus nodes.
- Emergence and Working of blockchain is not properly explained. The following works can be helpful. Untangling blockchain technology: A survey on state of the art, security threats, privacy services, applications and future research directions; Blockchain for smart cities: A review of architectures, integration trends and future research directions.
Response: Thanks very much for your comment. Based on your suggestion, we have updated the Introduction section with detailed discussions on the emergence of blockchain as follows:
Updates in Introduction section:
Ever since the invention of Blockchain, many researchers and organizations have contributed to the evolution of the ledger technology resulting in three different stages from Bockchain 1.0 through 3.0. The Blockchain 1.0 is the initial and simplest version of distributed ledger, the Blockchain 2.0 is the second generation distributed ledger, where the concept Smart Contract was introduced along with enhanced capabilities of Blockchain protocols and the Blockchain 3.0 is the advanced version of distributed ledger technology with improved sustainability, scalability, cost-effectiveness, more decentralization, and security \cite{bhushan2021untangling} However, a fusion of traditional IoT healthcare systems with decentralized blockchain technology will be a significant solution to overcome the security, privacy, reliability concerns of the IoT ensuring integrity, and authenticity of medical data.
- Introduction section looks lengthy authors must divide the introduction into separate paragraphs highlighting the background, motivation, major contribution and paper organization
Response: Thanks very much for your comment. Based on your suggestion, we have updated the Introduction section by including the Motivation, Major contributions and Paper organizations as the subsections.
Updates in Introduction section:
1.1 Motivation:
In a distributed decentralized ledger based peer to peer network, the block generation will happen based on the consent of all the peers. It is challenging to achieve the consensus in a trust less distributed ecosystem where all the peers connected to the network will participate in block validation process, however trustfulness in validation is attained using consensus algorithms, as an agreement to gain the mutual trust among the peers without using any trusted third party authority. The consensus algorithm of the blockchain technology will affect the overall performance interns of throughput, delay, and fault-tolerance of the blockchain network. Table \ref{tab:my-table1} shows the comparison between widely used consensus algorithms. The challenging task in every algorithm is to strengthen or improve the efficiency of core algorithms by incorporating various techniques without compromising the security and privacy of the system.
However, the authors Meshcheryakov et.al \cite{meshcheryakov2021performance} has reviewed the impact of PBFT consensus algorithm on the performance of Blockchain in a resource-constrained IoT environment by considering various performance attributes. The experimental results of PBFT based blockchain show high performance for networks with 70 nodes. Hence the PBFT based consensus can be applicable to various applications with implanted medical devices, selfcontained telemedicine devices. Even though the PBFT is widely used in most of the blockchain based healthcare applications, PBFT consensus needs to be improved in various aspects such as verifying the reliability of nodes before participating in the consensus process, penalizing the malicious nodes, improving the scalability, reducing the frequent network communication among the nodes. Verifiable Random Function (VRF) \cite{dodis2005verifiable} is a pseudorandom function, used in various blockchain consensus algorithms like Delegated Proof of Stake (DPoS), Proof of Stake (PoS) \cite{chen2019algorand} for choosing nodes engage in consensus activity. Although, VRF helps in improving the blockchain network security by randomly choosing the nodes for the consensus process. The selected nodes are not always trustworthy. However, trust evaluation of each and every node participating in the consensus process is very much necessary ensuring only the most reliable nodes are participating in the consensus process.
1.2 Major contributions and Paper organizations:
In general, our contributions to the proposed model are as follows,
- We propose Eigen trust-based reputation model to verify the reliability of each and every node using global trust value. The reputation model is related to the node behaviour in the system, also helps in reducing the number of untrusted nodes participating in the consensus process.
- We propose a VRF based election process to ensure fairness and randomness in electing the primary node from a group of trusted consensus node list. This process will reduce the communication complexity among nodes and also strengthen the security of the healthcare system.
- The proposed model is tested in a simulated environment and analyzed with the existing PBFT consensus algorithm intense of throughput, latency, communication overhead. The proposed algorithm results in better performance.
The rest of this paper is organized as follows, Section II describes the existing works on PBFT consensus and their usage in various healthcare applications. Section III introduces the proposed model. Section IV provides experimental analysis interns of fault tolerance, throughput, latency, and comparison with traditional PBFT consensus algorithm. Section V summarizes the proposed model highlighting the future directions
- Section 2 can be summarized in the form of a table. Authors must highlight and compare the contribution of various previous works that have been discussed
Response: Thanks very much for your comments. We have provided more included more discussions on the existing works along with the summary table in Literature Review section.
Updates in Literature review section:
{Xu et. al\cite{xu2021efficient} proposed an energy-efficient consensus algorithm for IoT-blockchain-based applications. The proposed algorithm uses the modified PBFT consensus by implementing VRF to choose consensus node ensuring the security of the primary node. Although the simulation results in improved performance in terms of latency and energy consumption compared to the existing PBFT algorithm but decreased throughput when the number of nodes increases. Cai et. al \cite{cai2020dynamic} proposed a Dynamic-reputation Practical Byzantine Fault Tolerance (DPBFT) algorithm by implementing a credit-based consortium method for the consensus node election process, a monitoring node that divides the node-set based on the reputation value. However, the proposed algorithms resolve the issues of traditional PBFT interns of a long consensus period, low efficiency, high CPU usage but the entire process of DPBFT is managed by monitoring node. Further, the DBPFT can be improved by implementing clustering techniques.
- The proposed methodology is not properly presented in the manuscript.
Response: Thanks very much for your comments. We have include the algorithm and steps involved in the proposed model with detailed discussions in Proposed Model section as,
Updates in Proposed Model:
The various steps involved in the proposed PBFT consensus algorithm are as follows
Step 1: Identify the Global Trust value of each node present in the network using algorithm 1
Step 2: Choose a Primary Node using VRF algorithm using algorithm and divide the nodes into Consensus Node List and Backup Node List using Global Trust value as parameter
Step 3: Primary node will execute the PBFT Consensus algorithm using set of consensus nodes.
Step 4: At the end of the consensus mechanism Primary node will create a Block with valid Transaction.
Step 5: Primary node will broadcast the updated block to both consensus node list and backup node list.
The figure \ref{fig:fig5} illustrates the various steps involved in the proposed secure PBFT based blockchain consensus algorithm for healthcare applications. Figure.\ref{fig:fig4} represent the Healthcare use-case for demonstrating the proposed algorithm where the real-time health related data of a Patient will be stored in the blockchain as transactions then it is made available to various healthcare users for further analysis. During the process the secure PBFT consensus algorithm with VRF and trust model will perform the transaction validation before adding the transaction to the Block. The node participating in the consensus operation should be a trusted node and it should not be vulnerable to malicious activities. The node participating in the consensus operation should be a trusted node and it should not be vulnerable to malicious activities. However, the initial level of trust analysis is achieved by computing global trust value for all nodes in the blockchain network, considering the local trust. In later stages, the secure cryptographic sorting algorithm VRF separates the initial node list as consensus node list and non-consensus node list. Only the nodes of the consensus node list can take part in the consensus process and a random primary node is also selected from the same set.
At first, the global trust value is used as an added parameter in the VRF algorithm for choosing the primary node and consensus node list for a particular view. The nodes of the non-consensus node list will not take part in the PBFT consensus process but they will be updated with the block details while adding the blocks to the blockchain. The process of dividing the nodes into groups will reduce the number of nodes taking part in the consensus algorithm hence the communication complexity is reduced. The proposed model also improves the security and privacy of data. If a selected primary node fails to produce a block in a particular view, then a new primary node will be selected from the consensus node list by dynamically updating the global trust value of nodes.
- Considered simulation parameters must be presented before the results and discussions.
Response: Thanks very much for your comments. We have updated the simulation parameters in Experimental Results section as
Updates in Experimental Results section:
The proposed secure PBFT consensus algorithm was tested in a simulated environment, implemented using a python programming language in an Ubuntu 16.04 operating system with configuration Intel Core i7 2.2GHz CPU with 16GB RAM and 1TB hard disk. Simulation is carried in a local machine by considering maximum of 35 nodes, initial global reputation of all the nodes as 1/nodes and numerical data (assumed as health-data related to a patient) required for the experiment purpose are generated manually and are fed into the system as transactions for analyzing the proposed algorithm to measure the security, reliability and performance aspects considering parameters such as transaction throughput, latency, faulty node ratio, and communication between the nodes.
- Authors fail to compare the proposed scheme with existing techniques. Why is the proposed scheme superior? This is not justified.
Response: Thanks very much for your comments. We have provided more discussions and analysis on the simulation results in Experimental Results section as
Updates in Experimental Results section:
Throughput Analysis
Figure.\ref{fig:fig6} shows the relationship between time and throughput in the traditional PBFT algorithm and proposed algorithm, the average throughput gap increases with respect to the time. Figure.\ref{fig:fig7} illustrates the changes of throughput with node transaction commit rate, irrespective of the request rate throughput rate of the proposed algorithm rises steadily compare to the PBFT. During the simulation after 1250 transactions, there is a steady increases transaction commit rate, this will be to minimal number of communication during the transaction validation process in proposed algorithm, hence time required for transaction confirmation is less compared to PBFT.
Transaction Latency
Figure. \ref{fig:fig8} shows the relationship between latency and the number of nodes, the latency of the proposed algorithm is less than the Traditional PBFT algorithm, indicating that nodes’ consensus speed is faster than PBFT algorithm. The main reason for achieving the minimal latency is grouping the nodes into consensus node list and backup node list , hence the number of nodes participating in the consensus process is minimal. During the simulations the number of nodes are varied from 2,4,6,8,... 30. As the number of nodes in the network increases the transaction latency will also in both PBFT and Proposed algorithm this is due to rise in internal communication required during verification. We also tested the proposed algorithm by considering the effect of block size on latency by considering the varied number of transactions on different blocks. Since the number of transactions is more in a block it takes a longer time to propagate and verification leads to higher latency.
Communication between nodes
The number of communications between the nodes to achieve consensus affects the overall performance of the system, which is interrelated to the number of resources consumed for the deployment of the algorithm. However, communication times are experimentally computed considering different nodes and compared with the traditional PBFT algorithm. Figure.\ref{fig:fig10} illustrates the communication times of PBFT and the proposed algorithm, the general observation is that the proposed model induces less communication overhead. The main reason for minimal communication between the nodes in proposed algorithm is due to the segregation nodes into two lists. However only a trusted consensus nodes will participate in transaction validation process. But, eventually if we vary the nodes from 2, 4, 6, .... 18 the number of communication will also increases. Finally, the proposed model consumes a less number of resources with minimal communication among the nodes.
To summarize, the experimental results of the proposed PBFT consensus algorithm has shown 25% increase in transaction confirmation time, steady increase in transaction latency as the number of nodes increases in the network with minimal communication overhead compared to the Traditional PBFT consensus algorithm. The comparative analysis of the proposed algorithm with the existing popular consensus algorithms by considering the parameters such as Nodes Participating in the consensus Process, Node Reliability Assessment, Network Type, Node Election Fairness, Communication Overhead, is shown in Table\ref{tab:my-table3}.
Reviewer 3 Report
The paper is done according to the instructions given in the journal guidelines. Organization of paper with sections (1. Introduction, 2. Literature review, 3. Proposed Model, 4. Experimental Results, 5. Conclusion and Future Scope) is different than recommended (Introduction, Materials and Methods, Results, Discussion, Conclusions), but it is adequate with. Complete material is ordered in a way that is logical, clear, and easy to follow.
Authors cited sources adequately and appropriately, and all the citations in the text are listed in the References section. English language and style are fine.
The authors provided theoretical background and research related related works. The main contribution of the paper is proposition of a new algorithm, its testing and comparing with traditional algorithms.
The model is presented well with appropriate experiment and analysis. Authors succeed to show that model has better performance.
My opinion is that paper is recommended for acceptance.
Author Response
The paper is done according to the instructions given in the journal guidelines. Organization of paper with sections (1. Introduction, 2. Literature review, 3. Proposed Model, 4. Experimental Results, 5. Conclusion and Future Scope) is different than recommended (Introduction, Materials and Methods, Results, Discussion, Conclusions), but it is adequate with. Complete material is ordered in a way that is logical, clear, and easy to follow.
Authors cited sources adequately and appropriately, and all the citations in the text are listed in the References section. English language and style are fine.
The authors provided theoretical background and research related related works. The main contribution of the paper is proposition of a new algorithm, its testing and comparing with traditional algorithms.
The model is presented well with appropriate experiment and analysis. Authors succeed to show that model has better performance.
My opinion is that paper is recommended for acceptance.
Response: Thanks very much for your positive comments. The comments valuable and it’s an encouragement for our future research journey.
