A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology
Abstract
:1. Introduction
- To design a blockchain-based data privacy preservation model with a hybrid meta-heuristic algorithm over the supply chain network to secure information exchange and guarantee the privacy of data access in the Ethereum platform. Here, the performance improvement of the proposed model is applicable to different applications regarding cryptocurrency, food supply chains, and sealed-bid auctions.
- To generate the key with the help of developed ABC-ROA for exchanging the secured data in the supply chain framework using data restoration and data sanitization procedures in the Ethereum environment. The developed ABC-ROA algorithm is utilized to restore the data from the receiver side. Consequently, it helps to access the original data that can be generated from the original key.
- To implement the hybrid meta-heuristic algorithm known as ABC-ROA for choosing the best optimal key to maximize the performance of the developed blockchain-based privacy preservation model. Here, the designed ABC-ROA algorithm improves the system’s robustness. It is also used to solve complex issues.
- To compare the developed ABC-ROA-based privacy preservation system with existing meta-heuristic algorithms using a variety of metrics to verify the performance of the developed model.
2. Literature Survey
2.1. Related Works
2.2. Statement of Problem
3. Privacy Preservation of Supply Chain Management Data: New Meta-Heuristic with Ethereum Blockchain
3.1. Data Used for Privacy Preservation
3.2. SCM Privacy Preservation Framework
4. Supply Chain Network Creation and Privacy Preservation Steps Handled
4.1. Supply Chain Networks
4.2. Data Sanitization and Data Restoration
5. Adaptive Border Collie Rain Optimization Algorithm with Ethereum Blockchain for SCM Data Privacy Preservation
5.1. Optimal Key Generation
5.2. Objective Function
5.3. Ethereum Blockchain Technology
5.4. Proposed ABC-ROA
Algorithm 1: Developed ABC-ROA |
Initialize the population and acceleration value |
Find the fitness solution |
Calculate the velocity Using Equation (29). |
For |
For |
If |
Assign the value of |
Else |
Assign the value of |
End if |
If |
Select the radius of the raindrop using Equation (50). |
Update the solution with the ROA algorithm using Equation (51). |
Else |
Update the solution with the BCO algorithm using Equation (38). |
Determine the best fitness of the sheep |
Update the velocity of the sheep in the BCO algorithm |
Evaluate the sheep’s position |
Update the position of the sheep using Equation (32). |
End if |
End |
End |
Obtain the best position |
End |
6. Results and Discussion
6.1. Simulation Setting
6.2. Effectiveness Analysis Using Euclidean Distance
6.3. Performance Analysis Using the Harmonic Mean
6.4. Effectiveness Analysis Using the Arithmetic Mean
6.5. Cost Function Analysis on the Proposed Model
6.6. Effectiveness Analysis Using Key Sensitivity
6.7. Performance Analysis Using CPA and CCA
6.8. Statistical Analysis of the Designed Method
6.9. ANOVA Test for the Developed Data Privacy Preservation Model over the Ethereum Network
6.10. Validation of Control for Parameters of Different Algorithms Using the Designed Method
7. Security Analysis
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Study | Techniques | Features | Disadvantages |
---|---|---|---|
Lin et al. [18] | PPChain |
|
|
Rahmadika et al. [19] | BiLSTM |
|
|
Xiong et al. [20] | SGINs |
|
|
Singh et al. [21] | Federated Learning |
|
|
Guo et al. [22] | Data encryption |
|
|
Mohan et al. [23] | Raspberry Pi network |
|
|
Elisa et al. [24] | peer-to-peer |
|
|
Dewangan et al. [25] | EdDSA |
|
|
Key Variations in the Percentage | HHO [29] | EF-HHO [30] | BCO [27] | ROA [28] | ABC-ROA |
---|---|---|---|---|---|
Dataset 1 | |||||
10 | 99.911 | 91.762 | 95.836 | 99.319 | 87.762 |
20 | 99.877 | 93.113 | 96.495 | 99.396 | 89.113 |
30 | 99.845 | 94.312 | 97.079 | 99.673 | 90.312 |
40 | 99.873 | 95.739 | 97.806 | 99.787 | 91.739 |
50 | 99.927 | 96.462 | 98.194 | 99.861 | 92.462 |
Dataset 2 | |||||
10 | 99.909 | 91.626 | 95.767 | 99.31 | 87.626 |
20 | 99.876 | 92.894 | 96.385 | 99.389 | 88.894 |
30 | 99.843 | 94.071 | 96.957 | 99.669 | 90.071 |
40 | 99.871 | 95.452 | 97.661 | 99.784 | 91.452 |
50 | 99.926 | 96.283 | 98.105 | 99.859 | 92.283 |
Dataset 3 | |||||
10 | 99.926 | 92.981 | 96.454 | 99.429 | 88.981 |
20 | 99.899 | 93.969 | 96.934 | 99.494 | 89.969 |
30 | 99.872 | 94.909 | 97.391 | 99.729 | 90.909 |
40 | 99.895 | 96.057 | 97.976 | 99.823 | 92.057 |
50 | 99.94 | 96.858 | 98.399 | 99.884 | 92.858 |
Key Variations in the Percentage | HHO [29] | EF-HHO [30] | BCO [27] | ROA [28] | ABC-ROA |
---|---|---|---|---|---|
Dataset 1 | |||||
10 | 58.784 | 52.465 | 55.624 | 58.236 | 43.465 |
20 | 62.388 | 59.351 | 60.87 | 61.866 | 50.351 |
30 | 65.623 | 63.651 | 64.637 | 65.13 | 54.651 |
40 | 68.534 | 65.454 | 66.994 | 68.075 | 56.454 |
50 | 71.114 | 65.723 | 68.418 | 70.684 | 56.723 |
Dataset 2 | |||||
10 | 57.875 | 49.222 | 53.548 | 57.313 | 40.222 |
20 | 61.469 | 55.313 | 58.391 | 60.935 | 46.313 |
30 | 64.722 | 60.534 | 62.628 | 64.219 | 51.534 |
40 | 67.695 | 63.39 | 65.542 | 67.223 | 54.39 |
50 | 70.332 | 64.698 | 67.515 | 69.891 | 55.698 |
Dataset 3 | |||||
10 | 68.646 | 61.093 | 64.87 | 68.191 | 52.093 |
20 | 71.919 | 66.26 | 69.089 | 71.501 | 57.26 |
30 | 74.772 | 70.154 | 72.463 | 74.389 | 61.154 |
40 | 77.269 | 72.702 | 74.986 | 76.918 | 63.702 |
50 | 79.448 | 74.553 | 77 | 79.127 | 65.553 |
Terms | HHO [29] | EF-HHO [30] | BCO [27] | ROA [28] | ABC-ROA |
---|---|---|---|---|---|
Dataset 1 | |||||
Best | 6.5506 | 5.5954 | 6.2903 | 5.8088 | 5.5696 |
Worst | 6.6609 | 6.6609 | 6.6609 | 6.6609 | 6.125 |
Mean | 6.5815 | 5.6644 | 6.3546 | 5.9459 | 5.6203 |
Median | 6.5506 | 5.5963 | 6.2927 | 5.8587 | 5.5836 |
Standard Deviation | 0.050579 | 0.21239 | 0.12052 | 0.18825 | 0.11126 |
Dataset 2 | |||||
Best | 6.5078 | 5.0281 | 6.2831 | 5.2618 | 4.8695 |
Worst | 6.6822 | 6.6822 | 6.6716 | 6.6822 | 5.7822 |
Mean | 6.5701 | 5.1008 | 6.3662 | 5.4352 | 4.9401 |
Median | 6.5303 | 5.0323 | 6.3072 | 5.3044 | 4.8807 |
Standard deviation | 0.071683 | 0.32951 | 0.11869 | 0.3084 | 0.18276 |
Dataset 3 | |||||
Best | 6.552 | 6.2897 | 6.5597 | 6.3459 | 6.2438 |
Worst | 6.7356 | 6.7356 | 6.7356 | 6.7356 | 6.5356 |
Mean | 6.6597 | 6.3163 | 6.625 | 6.4021 | 6.3077 |
Median | 6.6337 | 6.2905 | 6.6029 | 6.3933 | 6.2847 |
Standard deviation | 0.080236 | 0.088813 | 0.054733 | 0.082943 | 0.063698 |
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Viswanadham, Y.V.R.S.; Jayavel, K. A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology. Electronics 2023, 12, 1404. https://doi.org/10.3390/electronics12061404
Viswanadham YVRS, Jayavel K. A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology. Electronics. 2023; 12(6):1404. https://doi.org/10.3390/electronics12061404
Chicago/Turabian StyleViswanadham, Yedida Venkata Rama Subramanya, and Kayalvizhi Jayavel. 2023. "A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology" Electronics 12, no. 6: 1404. https://doi.org/10.3390/electronics12061404