## 3 Methodology
### Proposed Secure Search Algorithm
The simulations results are achieved before and after applying two-level privacy-preservation technique. The proposed model with the transformed IoT dataset has achieved 0.0167 loss and 99.58 acc, while 0.0052 loss and 99.89 acc with the actual dataset. Similarly, the transformed benchmark dataset model has obtained 5.5116 loss and 90.86 acc, while 0.0685 loss and 99.98 acc with the actual dataset. Table 4 provides the overall comparative analysis of our proposed framework and the benchmark models. We also evaluate the proposed model in terms of class wise prediction results, i.e., PR (Prediction Ratio), DR (Detection Rate), F1 (Acc) and FAR (False Acceptance Ratio) metrics. These metrics are based on True/False values. In Table 3, we see that the model with actual and transformed IoT data sets has achieved in an average of 90 percent-100 percent values for PR, DR, and F1 score and has reduced FAR close to 0 percent. Similarly, in Table 4, for various types of attacks such as DoS, DDoS, Reconnaissance, and Normal group of actual IoT-Botnet data set model has achieved in an average of 99 percent-100 percent values for PR, DR, and F1 metrics. ## 6 Conclusions and Future Works

In this paper, we have implemented a novel extended approach of homomorphic encryption in a digital healthcare system leveraging blockchain technology and DL, which provides secure keyword search facility at the user end. Our proposed scheme supports immutable, tamper resistant, and delivery secured data, which results in the reduction of security breaches to the healthcare data. The deep learning technique has been introduced to train the model, which can predict and monitor the attacks such as DoS, DDoS, and collusion resistance. For training and classification, we have divided our data set into two categories, i.e., training and testing data sets, where 70 percent of the data set has been used for training and 30 percent for testing purposes for cross-validation. Furthermore, our novel mechanism allows blockchain users to encrypt data at their own side and upload it to the distributed ledger for record purpose. Users can securely search the desired health-related data without decryption based on homomorphic SSE. We have provided a comparison to the benchmark models such as [13; 30; 50; 51; 52]. Our proposed approach is resistant to active collusion and replay attacks due to the flexible policy revocation. Blockchain technology also supports distributed data, redundancy, and fault tolerance features for digital systems. Following the proposed research, current challenges and problems in the literature faced by the digital healthcare industry were solved. More and more, we proposed a framework and algorithms that enables access control policy for users to achieve privacy and security for patient health data in the PHR system. The proposed method provides more independence to the the users, and it supports flexibility and fine-grained keyword searches. We have justified our proposed research algorithms and polices through simulations run on hyperledger fabric tool. We used the Pycharm tool for data analysis. With our proposed method as the most up-to-date approach applied first on healthcare and blockchain technology. We have improved the security and anonymity as compared to the benchmark models such as Medrec, Medchain, and Medbichain. In the future, the proposed model can be enhanced by applying different deep learning techniques such as classification methods. Conceptualization, A.A.; data curation, O.H.F. ; formal analysis, A.A., M.F.P., M.A.A., J.A. and M.M. ; investigation, J.A. and M.M.M. ; methodology, A.A. and A.D.J. ; project administration, M.M. and J.A. ; resources, M.F.P. and M.A.A. ; software, A.A.; supervision, M.F.P., J.A., A.D.J. and M.M. ; validation, O.H.F. ; visualization, A.A.; writing--original draft, A.A., O.H.F. and M.F.P. ; writing--review and editing, A.A., M.M. and J.A. All authors have read and agreed to the published version of the manuscript.