## 3 Methodology
### Proposed Secure Search Algorithm
In Figure 13, the simulation results for the searching time and the number of attributes are shown. We have compared the proposed research method of the search time and the attributes with the latest benchmark models based on homomorphic encryption and the healthcare system. From the simulations' results, its very clear that the proposed framework performs better than the benchmark models for the same number of parameters. The algorithm for the index searching is describe in our proposed algorithm for index searching techniques. The benchmark model's communication and search times are greater compared to the proposed framework. In Figure 14, we have explained the comparative analysis of the proposed methodology and the benchmark methods. For the same data set, we run our experiments in our proposed framework, and we compared the efficiency and number of packet sent to the nodes. It can be easily observed that the number of patient health records sent to the base station are more in number in the case of our proposed framework. In the case of benchmark-based models, it can be seen that it is very less comparatively. We have improved the efficiency 1.9 times as compared to the benchmark models. Table 3 provides the predictive value based on the proposed techniques, i.e., homomorphic encryption and we have predicted the number of false positives and false negatives. In addition, from Table 3 it is very obvious that our proposed method provides more accuracy as compared to the previous benchmark models. In Figure 15 we have described the comparative analysis of the concurrent request for our proposed policies. Our policies support the concurrent request. However, these requests are not limited to read or write; they also consist of update and delete policies. In Figure 16, the simulation results based on the homomorphic encryption for keyword search. From the results, it can be concluded that our proposed method support cross-domain as well ad fine-grained access control approach. The proposed model resist the \begin{table}
\begin{tabular}{l l} \hline \hline  & **Simulations Parameters** & **Explanation** \\ \hline Attrb & Attributes \\ B\({}_{v}\) & Block Search \\ K\({}_{u}\) & Keyword Search \\ I\({}_{u}\) & Index Search \\ N & Number of Transactions \\ HW & GPU enable Hardware \\ DN & Dead Nodes \\ CH & Cluster Head \\ HE & Homomorphic Encryption \\ BC & Blockchain \\ SC & Smart contracts \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of Proposed Parameters. tampering, security breaches and unauthenticated access. In Figure 17, we have explained the number of transaction sent from one domain to another domain. It can be easily observed that the number of transactions which mean the number of patient health records (PHR) or electronic health records (EHR) sent per round. We run our simulations for 800 rounds and evaluated the number of patient health records sent. We did comparative analysis with the benchmark models such as Medrec and Medblock. In Figure 18, we have described the experimental results for our keyword search mechanism in our proposed framework and benchmark models. Our benchmark models consist of the [PERSON] et al., [PERSON] et al., and Medblock and Medchain. We can see from the simulations results that our proposed framework performs better than the benchmark models in searching the encrypted keyword. Our proposed method provides less communication cost for each keyword search in the blockchain framework. Our method is also valid for the cross-domain. In Figure 19, we have described the simulation results based on our proposed policies. We have proposed access control policies for our proposed framework using homomorphic encryption and pseudo random algorithms. We have evaluated our proposed access control policies against the number of execution time and number of access policies. We did experiments on the policy revocation, policy creation, and add policy.