## 4 Supply Chain Network Creation and Privacy Preservation Steps Handled
### Proposed ABC-ROA  
The ABC-ROA algorithm is designed to enhance the effectiveness of the developed data privacy preservation system to select the optimal key in the data sanitization and data restoration process in the SCM. The existing algorithms face a few challenges in privacy preservation in Ethereum blockchain technology, where the existing heuristic algorithms have a limited number of resources to store the data. Hence, they face security and scalability challenges. The existing ROA and BCO algorithms are utilized for this work. Here, the BCO algorithm is selected in the developed method to increase the performance and robustness of the model. Additionally, it reduces the errors to increase the effectiveness concerning precision, f1 score, and accuracy. The BCO is solving the multi objective combinational optimization problems. However, it gains low scalability and  
Figure 4: Privacy preservation framework in Ethereum blockchain technology. utility. Due to the issues in the BCO algorithm, the ABC-ROA algorithm is developed by combining the ROA. ROA is chosen in the implemented model since it can perform in parallel computing and is mostly helped for high privacy protection. It is used to improve the user experience. These advantages in both BCO and ROA make the efficient performance in the suggested blockchain-based privacy preservation system by overcoming these conventional issues. The ABC-ROA algorithm is used to improve the performance of the developed blockchain-based data privacy preservation system. The ABC-ROA algorithm is implemented based on the current fitness and average fitness function. The term defines the current fitness \(Fit_{cr}\), and the average fitness is denoted by the term \(Fit_{avg}\). If \(Fit_{cr}<Fit_{avg}\), choose the random parameter \(s=2\) otherwise, select \(s=3\) as the random parameter value. However, in the conventional algorithm, the random parameter \(s\) is selected randomly in the interval between \([0,\,1]\). If \(s==2\) then, select the ROA algorithm otherwise, select the BCO algorithm. The developed ABC-ROA algorithm increases the fitness function. BCO [27]: Three dogs and sheep are considered in the Border collie optimization process. In a real-world scenario, one dog can manage the herd by himself. However, three dogs are considered because the search space for certain optimization issues might be large. When the algorithm is started, a group of three dogs and some lambs are exhibited here. The dogs are in charge of returning the sheep to the farm after they have gone out in different directions for grazing. Random variables are used to initialize the positions of sheep and dogs. According to their positions, the dogs are designated lead, left, and right. From the front, the lead dog directs the herd. The person with the fitness \(Fit_{g}\) is chosen to be the dog in front of the herd or the lead dog. The major task of these dogs is to observe and stalk the herd. The terms \(Fit_{sj}\) and \(Fit_{mf}\) denotes the fitness values. The fitness of the sheep is known as \(Fit_{t}\). The velocity of the lead dog is calculated using Equation (28). \[W_{g}(u+1)=\sqrt{W_{g}(u)^{2}+2\times NC_{g}(u)\times P_{g}(u)} \tag{28}\]  
Then, the velocity of the left dog is measured using the given Equation (29). \[W_{sj}(u+1)=\sqrt{W_{sj}(u)^{2}+2\times NC_{sj}(u)\times P_{sj}(u)} \tag{29}\]  
The term \(NC\) is the acceleration of the dog and the term \(P\) is the dog's position. The term \(W\) indicates the velocity of the dog. The right dog velocity is calculated using Equation (30). \[W_{mf}(u+1)=\sqrt{W_{mf}(u)^{2}+2\times NC_{mf}(u)\times P_{mf}(u)} \tag{30}\]  
Here, the variables \(W_{sj}(u+1)\), \(W_{mf}(u+1)\) and \(W_{g}(u+1)\) is denoted by the velocity of time at \((u+1)\) for the right, left, and lead dogs. Moreover, the terms \(NC_{g}(u)\),\(NC_{sj}(u)\), and \(NC_{mf}(u)\) denotes the acceleration for lead, right, and left dogs. \(P_{g}(u)\),\(P_{sj}(u)\), and \(P_{mf}(u)\) describes the position of lead, right, and left dogs.