# Security, Privacy, and Reliability in Digital Healthcare Systems Using Blockchain
## 5 Proposed Algorithms
### Authentication Mechanism
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.NO** & **Parameters** & **Details** \\ \hline
1 & \(BN\) & Blockchain Network \\
2 & \(C_{ID}\) & Clinician ID \\
3 & \(LID\) & Lab ID \\
4 & \(PHR\) & Patient Health Record \\
5 & \(R^{S}\) & Ring Signature \\
6 & \(U^{Name}\) & Username \\
7 & \(P^{K}\) & Private Key \\
8 & \(r\) & Integer \\
9 & \(N\) & Number of Nodes \\
10 & G & Bilinear Order Group \\
11 & \(P^{1}\) & Generator of Additive Group 1 \\
12 & \(P^{2}\) & Generator of Additive Group 2 \\
13 & \(id\) & Bilinear Identifier \\
14 & \(H\) & Homomorphic Encryption \\
15 & \(k\) & Degree of Signature \\
16 & G1 & Group 1 of Bilinear Pair \\
17 & G2 & Group 2 of Bilinear Pair \\
18 & \(r\) & Number of Rounds \\
19 & \(h\) & Digital Signature \\
20 & \(W_{o}\) & Random Weight \\
21 & \(S\) & Signature \\
22 & \(T_{id}\) & Token \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations and their explanation. ## 6 Data Type and Sub-Module of Our Proposed Framework

The data for our proposed framework are intended to be patient health records (\(PHR\)). \(PHR\) can be divided into three classes: \(PHR\) privacy attributes, explicit \(id\), and \(quasi-id\). Explicit \(id\) is normally used as a patient's identifying information that indicates the patient, such as an \(ID\) number, name, and cell number. Similarly, the \(Q-ID\) provides the patient's bio-data and home address, as well as their age, date of birth, and office address. Privacy-related information refers to a patient's sensitive attributes, which include types of illness and the patient's income or resources. To publish the patient's health data and to maintain their data, it is necessary to ensure that the individual attributes of the new dataset are appropriately processed. Most of the existing approaches do not provide any anonymity. Our proposed framework will provide a novel technological approach that includes anonymity, diversity, and confidence. Figure 7 shows the Block header and sequence of transactions leveraging CA. ## 7 Proposed Approach to the Experimental Setup and Analysis

The step-by-step procedure is described below. We first identify the features that affect the performance of a cross-domain framework in a healthcare system. We then pre-process and elaborate these features. We have developed a novel algorithm for a cross-domain blockchain-based framework and a clustering mechanism to be used for performance improvements. An experimental evaluation of the security and performance with the highest number of rounds is conducted by using a novel smart contract. The experimental evaluation is conducted by using the three-layer internet topology. To allow multiple sensors to be placed, the topology is partitioned into several domains; the sensors are placed using an effective sensor placement algorithm and by using smart contracts such that the delay between the blocks and transactions is minimal. The performance results of the proposed approach are compared with those of the benchmark schemes by using blockchain tools, Matlab, and Pycharm. Figures 5 and 6 represent the configuration and background of our proposed blockchain. We implemented our proposed blockchain by using a Hyperledger Fabric component and \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.NO** & **Parameters** & **Details** \\ \hline
23 & \(P_{K}\) & Public Key \\
24 & _Mod_ & Modulus \\
25 & \(D_{e}\) & Decryption \\
26 & \(E_{T}\) & Transmission Energy \\
27 & \(p^{T}\) & Transmission Power \\
28 & \(p\) & Probability \\
29 & _Elec_ & Node Election \\
30 & _SM_1 & Modular Signature \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._

Figure 7: Block header and transaction sequence using CA. the Docker tool. The code was edited with chaincode, and we performed our analysis with the Spyder IDE tools. ### Computational Overhead

We evaluated the communication cost by using mathematical modeling; through simulations, we evaluated all of the operations.