Semantha et al. [17] proposed a conceptual framework for privacy-preserving in patient record management systems (PRMS). The proposed framework is mainly used to identify the key components and variables for privacy-preserving policies. The conceptual framework provides a data protection scheme which ensures patient data from third-party members. The proposed framework reduces the error ratio in the data protection process. The proposed framework increases the effectiveness and efficiency range of PRMS.
Wang et al. [18] developed a blockchain-based privacy-preserving scheme for healthcare data in encrypted cloud file systems named Efficient Encrypted Parallel Ranking (EEPR). Ensuring patients’ medical data is a crucial task to perform in healthcare centres. Blockchain uses a qualitative analysis technique which analyzes the request which is made by healthcare centres via the Internet of Medical Things (IoMT). The qualitative analysis reduces the latency in identification which improves the performance ratio of healthcare centres.
Saidi et al. [19] introduced a decentralized self-management of data access control (DSMAC) for healthcare data. Blockchain-based self-sovereign identification (SSI) model is implemented to identify the patient’s details from the database. SSI contains decentralized identifiers which provide access control policies to patients that ensure security. Experimental results show that the introduced DSMAC maximizes the significance and privacy level of patients’ medical data from third-party members.
Moqurrab et al. [20] designed a deep learning (DL) based privacy-preserving model for smart healthcare systems. The fog computing technique is also used here to improve the efficiency range of healthcare systems. Medical health records are maintained in every healthcare centre that contains appropriate health details of patients. Fog computing increases the diagnosis process’s accuracy. The proposed model improves the overall privacy and security ratio of medical data.
Sun et al. [21] proposed a privacy-preserving bilateral fine-grained access control scheme (PBAC-FG) for cloud-enabled healthcare centres. The proposed scheme is mostly used in the cloud-enabled industrial Internet of Things (IIoT) healthcare. IIoT is mainly used to reduce both the time and energy consumption ratio in health monitoring systems. Match-making encryption technology is used in PBAC-FG which ensures the safety of patients. The proposed PBAC-FG increases the efficiency level of healthcare centres.
Zou et al. [22] developed a blockchain-based medical data sharing and privacy-preserving method (SPChain) for e-healthcare systems. Electronic medical records (EMR) contain significant medical records which are relevant to patients’ health conditions. The actual aim of the proposed method is to provide secure data-sharing processes in e-healthcare applications. The developed SPChain method achieves high security and reduces the time complexity in data-sharing processes.
Wu et al. [23] proposed a medical big data privacy protection (MNSSp3) using the Internet of Things (IoT) in healthcare systems. MNSSp3 is commonly used in healthcare centres to provide secure data-sharing solutions for users. MNSSp3 focuses on sharing data without any complexity and minimizes the computational cost in online platforms. When compared with other approaches, the proposed MNSSp3 approach increases the effectiveness and reliability range of the systems.
Khan et al. [24] introduced a new authentication and encryption framework for IoT-based medical sensor data. Elliptical curve cryptography (ECC) scan is used here to capture medical data. ECC provide necessary medical information and key values for the authentication process. User’s data, id, and secrete keys for authentication and identification processes. The introduced authentication framework increases the accuracy of authentication which enhances the performance level of healthcare centres.
Joshi et al. [25] proposed a delegated authorization framework using ABE for EHRs. The proposed framework is a patient-centric approach which manages the EHR in an application. ABE provide access control to access EHR data from the database that reduces the latency in the computation process. The proposed framework provides the necessary authority to the patients to improve the efficiency range of the healthcare centres.
Tao et al. [26] developed a practical medical file-sharing scheme for healthcare centres. Blockchain and decentralized ABE which is mainly used to ensure privacy-preserving scheme for users. The blockchain approach is used here to provide proper authentication services to the users that gather medical information for further processes. The developed scheme maximizes the safety and security level of patient data from third-party members.
Wang et al. [27] designed a physiological signal-based double chaotic encryption (PSDCE) for e-healthcare services. Electroencephalogram (EEG) signals are for the encryption process. Deep multi-domain features captured by EEG provide required data for diagnosis and privacy-preserving processes. A random generator is used here that identifies the key components from the database for the encryption process. The proposed PSDCE increases the robustness and efficiency level of e-healthcare services.
Adeniyi et al. [28] developed a block cypher algorithm based on a modified advanced encryption standard (AES) approach for medical information security. EHR is managed by healthcare centres that contain current medical health records of the patients. Experimental results show that the developed AES approach increases the accuracy of the diagnosis process. AES is mainly used here to improve the performance and effectiveness range of medical healthcare centres.
Li et al. [29] introduced a secure and efficient dynamic searchable symmetric encryption (SEDSSE) scheme for healthcare systems. The k-nearest neighbour (KNN) algorithm and ABE techniques are used in SEDSSE to secure medical data from third-party members. ABE provides appropriate solutions to solve the problems which are occurred in the systems. The introduced SEDSSE scheme increases the privacy and security levels of patients’ medical data in healthcare systems.
Tang et al. [30] proposed a new medical record-sharing scheme based on a searchable encryption approach. The proposed scheme is commonly used in e-healthcare centres. Functional access control is used here to access the EHR from the database. EHR contain the actual health condition details of patients which provide relevant data for disease detection and prediction processes. The proposed scheme maximizes the security range of medical data in e-health systems.
The afore-discussed methods/ techniques are valid for providing individual authentication/ access control for users regardless of multiple security constraints. This includes successful access, response, and lossless information. However, in some cases, complexity increases due to multiple authentication modifications in a single access session. Therefore for preventing such issues and to improve the concurrency in key generation and user identity verification, this article introduced TP3SS. Different from the existing method’s access and validation, the smart contracts used in this scheme validate the less complex differentiations most often for authentication.
Proposed Two-Phased Privacy Preserving Security Scheme
An electronic health record (EHR) is a programmed version of a patient’s paper chart. EHRs are real-time, resident-centred records that make particulars accessible immediately and safely to commissioned users. Particulars associated with a patient’s health, diagnosis, and medical assistance are essential for which individual concealment and safety are the main contemplations. This article introduces a Two-Phased Privacy Preserving Security Scheme (TP3SS) for EHR stored in clouds. Smart contracts are simply organizations stockpiled on a Hyperledger fabric blockchain that run when foreordained prerequisites are met. It generally is used to barbarize the electrocution of a concurrence so that all the patients can be instantaneously certain of the conclusion, without any mediator’s engagement or time loss. They can also barbarize a procedure, stimulating the next process when prerequisites are encountered. Access control is a safety technique that maintains the expedients in enumerating surroundings. It is a representative discernment in a safeguard that decreases the risks to the organization or electronic healthcare centres. Hyperledger fabric blockchain is a concerted, immutable ledger that promotes the procedure of documenting the agreement and tailing assets in an organization network. Hyperledger fabric blockchain is optimal for executing that information because it produces the prompt, consolidated and completely translucent knowledge gathered on an immutable ledger that can be permeated only by sanctioned network members. Privacy-preserving automation confesses users to preserve the privacy of their personal information given to and supervised by service providers all while confessing the organization to undemanding the performance of the given information by the users. Attribute-based encryption (ABE) is an authoritative encryption arrangement with resilient encapsulation over encrypted data that has been broadly embraced in cloud computing scenarios to promote data sharing. Figure 1 presents the proposed scheme’s process flow.
The users'/patients' health information is stored in the electronic healthcare records through cloud computing storage resources. For this storage process, some of the procedures are done by using the TP3SS. This method helps in secure access control and attribute-based encryption for privacy-preserving and preventing data misrepresentation. In this TP3SS procedure, secure access, smart contract and encryption operations are happening for the data privacy concern. Secure access is processed when permission is given by the users for a particular time. Secure access control is accomplished by producing mutual key-dependent smart contracts between the user and the EHR storage through cloud computing. Then the encryption process takes place based on the key generation. This is attribute-based encryption where the EHR permits to access the data for a particular period. This procedure also depends on the smart contracts between the users, the doctor and the EHR. Both secure access and attribute-based encryption are clubbed together in the smart contract depending on the sessions of the secure access. This procedure is done to verify the user's identity whether it is authenticated or not. The user identity, session and key generations are maintained in the ledger of the Hyperledger fabric blockchain which can be used in the smart contract processes. The validation which occurs in the smart contract is used for ensuring the EHR and user attributes which are combined in the current and previous smart contract access sessions. The Hyperledger fabric blockchain is used in preventing internal enumerative complexities and verifies the authorization of the users. The attributes which are identified by the blockchain are used for the key generation for the sharing and accessing of the records securely. This method reduces the entry of fraudulent users and also increases the security level under the perfect enumerative procedures. The users'/patients' information is stored in the EHR through cloud computing and then it is processed by attribute-based encryption to increase the privacy of the data and eliminate unauthorized users. The information is used in the smart contract procedure based on the validation of the permission and also this helps in the encryption operations. The process of reserving the patients’ information in the EHR through the cloud is explained by the following equation given(1) below:
$$\left.\begin{array}{c}V\left(t,\eta ,{\eta }^{\left(1\right)},\dots ,{\eta }^{\left(n-1\right)}\right)={\eta }^{\left(n\right)}\\ where,\\ {\eta }^{\left(i\right)}=\frac{{d}^{2}\eta }{{dt}^{2}}\end{array}\right\}$$
1
Where \(V\) is denoted as the users/patients' health data, \(t\) is denoted as the electronic health care data, \(\eta\) is denoted as the information stored in the cloud, \(i\) is represented as the authorization of the user. The variable\(n\) denotes the number of access instances. Now the secure access procedure takes place and for this process the user identity is vital. The delegation may occur in this user identity and the user permits for a particular period. The permission is given by the user to access the information for the allotted period based on the user's identity. The validation of the information access is acceptable until the contract ends. The accessing time is allotted by the users/patients based on the smart contracts which are between the users the doctor and the EHR. This is established by the key-dependent which is produced by the smart contracts within the time validation. The user must allow the accessing process of the given health information by providing a certain time in the contract. The user identity is a vital thing in the process of secure access with the authorization of the user. This process helps in determining the user identify whether the user is authorized or not. The user must allow the procedure based on their identity to access their health information. The smart contract is processed between the user and the organization within a limited time.
The secure access is preceded by the permission of the user/patient and doctor withholding their health information for further encryption procedures. The identity of the user plays a vital role in the security accessing process and the time will be given by the user. Within the given time by the user, secure access is processed depending on the user's identity. This is associated with the smart contracts validation time and then the encryption process is consummated. The user identity information is stored in the Hyperledger fabric blockchain where the ledger contains the other data of the healthcare. From this, the key generation procedure is processed based on the smart contracts which are occurred between the user and the EHR. The particular time is established by the user for accessing the information and then their identity. The user identity can be found in this procedure depending on the contract period and then this output can be helpful in the encryption procedure. A particular time is given to the secure access process to denote whether the user is legitimate or illegitimate. The information about the user is stored in the ledger of the blockchain and then it is used in the secure access procedure. The process of secure access based on the user identity is explained by the following equations given (2), (3) below:
$$\left.\begin{array}{c}{\widehat{G}}_{n}\eta =V\left(t\right);\\ {\widehat{G}}_{n}\eta =\sum _{i=0}^{n}V\left(i\right){\eta }^{\left(i\right)},\\ \begin{array}{c}V=\left({\widehat{G}}_{n}\eta \left(t\right)-{V\left(t\right)}^{2}\right)+\left({\widehat{G}}_{0}\eta \left(t\right)-{\eta \left(i\right)}^{2}\right)\\ V={V}_{i}+{V}_{t}\\ =\left({G}_{n}^{{\prime }}\sum _{i=0}-{V}^{{\prime }}{\left(t\right)}^{2}\right)+\left({\widehat{G}}_{0}\sum _{n=0}-{V}^{{\prime }}{\left(i\right)}^{2}\right)\end{array}\end{array}\right\}$$
2
$$\left.\begin{array}{c}\frac{\partial M}{\partial V}=\frac{2({G}_{n}^{{\prime }}K{)}^{t}\left({G}_{n}^{{\prime }}K-{V}^{{\prime }}\left(t\right)\right)}{{V}^{{\prime }}\left(t\right)\sum _{i=0}{\widehat{G}}_{n}}\\ \frac{\partial {M}_{i}}{\partial V}=\frac{2({G}_{0}^{{\prime }}K{)}^{t}\left({G}_{0}^{{\prime }}K-{V}^{{\prime }}\left(t\right)\right)}{{V}^{{\prime }}\left(t\right)\sum _{n=0}{\widehat{G}}_{n}}\\ K=({\widehat{G}}_{K}^{t}{\widehat{G}}_{K}+{\stackrel{-}{G}}_{K}^{t}{{\stackrel{-}{G}}_{K})}^{2}\left({G}_{K}^{t}{V}^{{\prime }}\left(t\right)\right)+({\stackrel{-}{G}}_{K}^{t}{\eta }_{t}^{{\prime }})\end{array}\right\}$$
3
Where \(G\) is denoted as the secure access procedure, \(K\) is represented as the user identity, \(M\) is denoted as the period given by the user, \(n\) is denoted as the authorization given by the user to the secure access. The access procedure pursued by the EHR-demanding user is presented in Fig. 2.
The access procedure converges\(K\) verification and encryption process for establishing a mutual smart contract. The encryption process takes place based on the key generation which is stored in the Hyperledger fabric blockchain ledger. Here the EHR should permit the encryption process which is done by TP3SS. The attribute-based encryption takes place with the permission of the electronic health record of the patients. The attributes are stored in the cloud of the patients and then it is used in this procedure. The period of encrypting the data is given to the organization based on smart contracts. Depending on the contract period both secure access and attribute-based encryption is takes place (Fig. 2). The outcome of the encryption process is clubbed together with the secure access output in the smart contract verification procedures. This process is done to extract the perfect attribute which matches the users’ information about their health. It also executes the capability of determining the various attributes and the keys depending on the user/patient's health records from the healthcare organizations.
After permission is given by the EHR to the encryption process, the procedure is taking place depending on the smart contract that takes place between the user and doctor and the EHR. The period is given to the encryption operation by the healthcare organization. This encryption procedure helps make high-security access to the patient’s information without any fraudulent users. This process is based on the key generation which is stored in the Hyperledger fabric blockchain ledger. The key authentication is provided using record-related attribute encryption that is valid within the contract period. After proceeding with both processes' secure access and encryption, the output is combined in the smart contract for further operations of enhancing the security level. These outputs help in determining the authorized users for the process and then further steps are taken to reduce the falsified users. The process of attribute encryption with permission from the healthcare organization is explained by the following equations given (4), (5) below:
$$\left.\begin{array}{c}\left({G}^{t}+{G}^{x}+{G}^{xt}+V\left(t,x\right)\right)\eta \left(x,t\right)=V(x,t)\\ K={K}_{i}+{K}_{n}+{K}_{t}\\ \begin{array}{c}={\left(\widehat{G}\eta -V\left(t,x\right)\right)}^{2}+{\left(\eta \left(0,x\right)-G\left(x\right)\right)}^{2}\\ =\sum _{\eta =1,t}{\left(\eta \left(t,\eta \right)-{B}_{\eta }\left(t\right)\right)}^{2}\end{array}\end{array}\right\}$$
4
$$\left.\begin{array}{c}\frac{\partial {Z}_{n}}{\partial V}=\frac{2{\widehat{G}}_{K}^{t}({\widehat{G}}_{K}^{t}V-V(x,t)}{\eta \left(t\right)}\\ \frac{\partial {Z}_{i}}{\partial V}=\frac{2{K}_{0}^{t}({K}_{0}V-G(0,x)}{B\left(t\right)}\\ \begin{array}{c}\frac{\partial {Z}_{t}}{\partial \eta }=\sum _{\eta =1,t}\frac{2{G}_{\eta }^{t}\left({K}_{\eta }V-{B}_{\eta }\left(t\right)\right)}{\eta \left(i\right)}\\ where {K}_{\eta }=K\left[t,\eta \right] \end{array}\end{array}\right\}$$
5
Where \(Z\) is denoted as the encryption procedure,\(B\) is represented as the permission granted from the cloud to perform an attribute-based encryption process. Now the key generation takes place which helps in the attribute-based encryption process. This key generation data is stored in the blockchain along with the session and the user identity information. Here in this key generation the text and the session attributes are considered for the encryption procedure. The text attributes consider the attribute keys and the values of the attributes in the healthcare organization to determine the users in the secure access. This helps in the determination of the security level of the process and the authorization of the users. The session attribute is used in the storing of the values which is gathered throughout the accessing process and the stored procedures. This will have the entire information of the attribute used in the session of the secure access and the storage and the encryption procedures.
The key generation is used to provide the information of the attribute for the attribute-based encryption process and then it will help determine the security range of the entire procedure. This is the attribute-based key generation which is stored in the blockchain format along with the session and the user identity. Then from this, the information will be passed to the encryption process based on the contract validity. The encryption process is used to make the output of the key generation into secret information which can only be read by authorized users. This helps in the reduction of falsified users with delegated healthcare information. This encryption is used to protect the users’ private information and to enhance the security of the contract between the users and the healthcare organization. The process of verifying the attribute-based key generation for the attribute-based encryption process is explained by the following equations given (6),(7)below:
$$\left.\begin{array}{c}\sum _{i=0}\left(W\right)={\left({\widehat{G}}_{K}^{t}{\stackrel{-}{G}}_{K}+\sum _{\eta =0,i,t}{K}_{\eta }^{t}{K}_{\eta }^{{\prime }}\right)}^{-1}\\ =\left({\widehat{G}}_{K}^{t}V\left(t,x\right)+\sum _{\eta =0,i,t}{K}_{\eta }^{t}{Y}_{\eta }(t,x)\right)\\ \begin{array}{c}where\\ {Y}_{0}=G\left(x\right),\\ \begin{array}{c}{Y}_{i}={B}_{i}\left(t\right),\\ {Y}_{n}={B}_{n}\left(t\right)\end{array}\end{array}\end{array}\right\}$$
6
$$\left.\begin{array}{c}\left[\begin{array}{cc}e& 0\\ 0& e\end{array}\right]\left[\begin{array}{c}{\eta }_{1}\\ {\eta }_{2}\end{array}\right]=\left[\begin{array}{cc}{G}_{1}+{G}_{2}& -{G}_{2}\\ -{G}_{2}& {G}_{1}+{G}_{2}\end{array}\right]\left[\begin{array}{c}{\eta }_{1}\\ {\eta }_{2}\end{array}\right]\\ \left[\begin{array}{cc}e& 0\\ 0& e\end{array}\right]\left[\begin{array}{c}{t}_{1}\\ {t}_{2}\end{array}\right]=\left[\begin{array}{cc}{K}_{1}+{K}_{2}& -{K}_{2}\\ -{K}_{2}& {K}_{1}+{K}_{2}\end{array}\right]\left[\begin{array}{c}{t}_{1}\\ {t}_{2}\end{array}\right]\\ \left[\begin{array}{cc}e& 0\\ 0& e\end{array}\right]\left[\begin{array}{c}{i}_{1}\\ {i}_{2}\end{array}\right]=\left[\begin{array}{cc}{\eta }_{1}+{\eta }_{2}& -{\eta }_{2}\\ -{\eta }_{2}& {\eta }_{1}+{\eta }_{2}\end{array}\right]\left[\begin{array}{c}{i}_{1}\\ {i}_{2}\end{array}\right]\end{array}\right\}$$
7
Where \(W\) is denoted as the key generation operation, \(e\) is denoted as the text attributes, \(Y\) is denoted as the session attribute, \(x\) is denoted as the outcome of the encryption procedure. Now the both results of the secure access and the attribute-based encryption are clubbed together in the smart contracts. Figure 3 presents the key generation process.
The secure access from the users/patients and key validity from the EHR is combined with the smart contract designated interval by authenticating the user identity. The key validity verification and access confinement are accompanied using ledge-stored user information. This information is stored in the blockchain ledger for further processes of preventing fraudulent users. The blockchain is used in the prevention of computational complexities and reduces falsified users. The security access and the encryption process are preceded until the validation of the contract ends (Fig. 3). The contract is processed based on the outcome of the secure access and the attribute-based procedure. Then it is done based on the session that occurred during the procedure and then the information of the session is stored in the ledger along with the key generation and the user identity. These processes take place to avoid illegitimate users in healthcare organizations and to enhance the security level. This also enhances the efficiency of the smart contract where further processes are takes place. The validation of the contract concludes the period of secure access and the encryption procedures. The process of smart contract by combining the outcome of the secure access and the attribute-based encryption depending on the session is explained by the following equations given(8),(9) below:
$$\left.\begin{array}{c}\ddot{\eta }=-\eta -{\eta }^{3}\\ L=\frac{{\eta }^{2}}{2}+\frac{{\eta }^{2}}{2}+\frac{{\eta }^{4}}{4}\\ \begin{array}{c}\frac{{\partial }^{2}\eta }{{\partial x}^{2}}+\frac{{\partial }^{2}\eta }{{\partial y}^{2}}=L(x,y)\\ {L}_{n,t}=\frac{1}{4}\sum _{K=1}^{\eta }\left({-1)}^{K+1}2G\left(Kx\right)G\right(Ky)\end{array}\end{array}\right\}$$
8
$$\left.\begin{array}{c}\eta \left(x,y\right)=\frac{-1}{2R{\left(\pi \right)}^{2}}2G\left(R\pi x\right)\left(R\pi y\right)\\ \frac{\partial }{\partial K}\left[\begin{array}{c}\eta R\\ \eta G\end{array}\right]=\left[\begin{array}{cc}0& -G∕2n\\ -G∕2n& 0\end{array}\right]\frac{{\partial }^{2}}{{\partial x}^{2}}\left[\begin{array}{c}\eta R\\ \eta G\end{array}\right]\\ G\frac{\partial }{\partial t}\eta \left(x,t\right)=\left[\begin{array}{cc}\frac{{-R}^{2}}{2n}& \frac{{\partial }^{2}}{{\partial x}^{2}}\end{array}\right]+V\left(x\right)+\eta (x,t)\end{array}\right\}$$
9
Where \(L\) is denoted as the smart contract between the user and the EHR, \(R\) is represented as the outcome of the secure access and the encryption procedures, \(\pi\) is denoted as the validation of the contract period. Now the blockchain helps in preventing the complexities that occur computationally. The information about the key generation, user identity and the session is stored here in the ledger. This helps in the identification of the user whether an authorized user or not. By the key generation, the attribute-based encryption takes place as the session helps in the smart contract procedures. The process done by the blockchain is explained by the following equations given (10),(11),(12) below:
$$\left.\begin{array}{c}\begin{array}{c}\left[\begin{array}{c}\eta R\\ \eta J\end{array}\right]=\left[\begin{array}{cc}J& 0\\ 0& J\end{array}\right]\left[\begin{array}{c}WR\\ WJ\end{array}\right]\\ NJ=({\widehat{G}}_{K}^{t}{G}_{K}+{W}_{0}^{t}{W}_{0}+{W}_{K}^{t}{W}_{K}+{G}_{0}^{t}{{G}_{i})}^{-1}({G}_{o}^{t}{\eta }_{0})\\ where,\end{array}\\ {J}_{0}=J(0,x)\\ \begin{array}{c}{J}_{G}=J\left(t,G\right)-J(t,R)\\ {J}_{t}={J}_{x}\left(t,G\right)-{J}_{x}(t,R)\end{array}\end{array}\right\}$$
10
$$\left.\begin{array}{c}\mu \left(x,0\right)=\frac{1}{{\pi }^{1∕2}\sqrt{\mu }}{e}^{-(x-{{x}_{0})}^{2}∕({2\mu )}^{2}+iG/G},\\ \mu \left(x,t\right)=\frac{{e}^{\frac{-(x-{{x}_{0})}^{2}∕({2\mu )}^{2}+iG/G}{\left({2\mu )}^{2}\right(1+t∕{\mu n)}^{2}}}}{{\pi }^{1∕4}\sqrt{\mu (1+it∕{n\mu )}^{2}}}{e}^{i(G0-Rt)}\end{array}\right\}$$
11
$$\left.\begin{array}{c}\ddot{\sigma }=\alpha \mu \\ \sigma =\left[\begin{array}{cc}G& 0\\ 0& G\end{array}\right]\left[\begin{array}{c}{W}_{x}\\ {W}_{y}\end{array}\right]\\ \begin{array}{c}{\sigma }_{n}=\left[\begin{array}{cc}K& 0\\ 0& K\end{array}\right]\left[\begin{array}{c}{t}_{x}\\ {t}_{y}\end{array}\right]\\ {\sigma }_{t}=\left[\begin{array}{cc}L& 0\\ 0& L\end{array}\right]\left[\begin{array}{c}{\mu }_{x}\\ {\mu }_{y}\end{array}\right]\end{array}\end{array}\right\}$$
12
Where \(J\) is denoted as the gathering operation of the blockchain,\(\mu\) is denoted as the information of the session of the smart contract, \(\sigma\) is denoted as the gathered data of user identity and the key generation in the ledger. The smart contract validation process is illustrated in Fig. 4.
The smart contract assessment is validated for the number of users,\(e\) and\(R\). This is observed for \([B,Y]\in M\) stored in the blockchain; the stored information is used for \(\pi\) process. In this process,\(\mu\) and\(\sigma\) presence are analyzed for\(L\). If \(\mu\) and \(\sigma\) are present (satisfied) then the existing contract is pursued. Contrarily in the absence of \(\mu , \left(e,Y\right)\) are updated whereas if\(\sigma\) is unavailable, then\(L\) is terminated (Fig. 4). The attribute which is identified by the encryption procedure helps in the sharing and accessing of the information records. After this, the adversary users are identified easily and then further steps are taken to reduce it. This procedure of accessing and sharing the records is explained by the following equations given (13),(14) below:
$$\left.\begin{array}{c}\sum _{i,j}\left({\sigma }_{n}\right)=({\widehat{G}}_{K}^{t}{G}_{K}+{L}_{0}^{t}{L}_{0}+{G}_{K}^{t}{)}^{-1}({L}_{0}^{t}{\mu }_{0}+{L}_{0}^{t}{\pi }_{0})\\ where \\ \begin{array}{c}{G}_{K}=\left[\begin{array}{cc}\ddot{K}& 0\\ 0& \ddot{K}\end{array}\right]\\ {L}_{0}=\left[\begin{array}{cc}{L}_{0}\left(0\right)& 0\\ 0& {L}_{0}\left(0\right)\end{array}\right]\\ {L}_{0\pi }=\left[\begin{array}{cc}\dot{L}\left(0\right)& 0\\ 0& \dot{L}\left(0\right)\end{array}\right]\end{array}\end{array}\right\}$$
13
$$\left.\begin{array}{c}F={\left(\left[\begin{array}{c}Y\\ {Y}_{0}\end{array}\right]-\left[\begin{array}{c}\stackrel{-}{Y}\\ \stackrel{-}{{Y}_{0}}\end{array}\right]\right)}^{2}\\ F=\left(\left[\begin{array}{cc}\widehat{Y}& \widehat{{Y}_{0}}\end{array}\right]-\left[\begin{array}{cc}Y& {Y}_{0}\end{array}\right]\right)\left(\left[\begin{array}{c}Y\\ {Y}_{0}\end{array}\right]-\left[\begin{array}{c}\stackrel{-}{Y}\\ \stackrel{-}{{Y}_{0}}\end{array}\right]\right)\\ \begin{array}{c}\left({P}^{t}P+{P}_{0}^{t}{P}_{0}\right){W}_{t}=\left({P}_{Y}^{t}+{P}^{t}{Y}_{0}\right)\\ {F}^{t}PW\left(t\right)={W}^{t}\left(Y\right)\end{array}\end{array}\right\}$$
14
Where \(F\) is denoted as the sharing and the accessing of the records, \(P\) is denoted as the outcome of the blockchain procedure. This process helps in the sharing of information with high privacy by using the attribute-based encryption process. The secure sharing process from the EHR cloud is illustrated in Fig. 5.
The \(EH{R}^{{\prime }}s\)are shared through\(M\) for verifying if \(B\) is available/ not. If it is not available then the sharing awaits acknowledgment. If \(B=true\) then \(Y\)and \(e\)are assimilated for \(n\in users\)as \(R\)for the users. Such authenticated users enter the\(F \forall G\) until the smart contract is valid (Refer to Fig. 5). The blockchain procedure helps in storing the information of the needed user identity, key generation and the session of the smart contract. Then it also reduces the computational complexities and prevents the adversary users with the falsified information. The time taken for the encryption process is less and then the key generation is helpful in the determination of the attributes. The self-analysis presents the discussion of a few metrics discussed in the above equations. First,\(n\left(\%\right)\) for the varying access requests (100 to 1200) is presented in Fig. (6).
The \(n\left(\%\right)\)analyses are presented for the varying intervals (10,20,30), and\(e=max\) and\(Y=max\) considerations. The \(n\left(\%\right)\) is increased by verifying \(k\) through\(\left(G,i\right)\); for\(\eta\) observed the \(Z\)and \(B\)are consecutive. Based on \(e\) and \(Y\)(variations) the\(L\) is either extended or canceled. Considering the \(W\)for different\(M\), the allocations for the \(Y\) are performed that maximize\(\sigma\) for the requesting users. The available user requests are validated using\(R\) and \(F\) across multiple intervals for preventing failures. Post this process the analyses of \(Z\)process time for the varying requests and access delegations are presented in Fig. 7.
The analyses for\(Z\) time for the varying requests and access delegations. The proposed scheme performs\(K\) validation based on\(e\) and\(Y\). Based on the unnecessary/ failing\(M\) the \(e\)and\(Y\) are modified for verifying\(n\). Computing the \(n\) for the given\(x\), the \(\pi\) verifications are performed. Therefore\(\sigma\) is validated for improving the sharing process. For the varying intervals, the \(P\) outputs are used for handling\(i\) and its flaws. If a time lapse is observed then the \(W\) for the next session requires some time. This shows up some variations in\(Z\) time for the various intervals (Fig. 7). In Fig. 8 the maximum\(\pi\) time is analyzed.
The \(\pi\)time varies for the requests based on\((e,y)\). The joint\((e,Y)\) is used for handling \(\mu\) and\(\sigma\) conditions. The above validation is performed only if \(\mu\) and\(\sigma\) is true such that the requests are provided with\(G\). Depending on the varying\(j\), the \(K\in M\) is modified using\(Z\) and overall process time. It is to be noted that this process requires less time compared to that of the consecutive\(G\) in\(T\). Therefore\(e=max\) shows up less varying phases compared to \(y=max\) and hence it required a high\(\pi\) (Refer to Fig. 8).
Datasets:
Electronic Health Record contains multiple fields and a patientId field is used to identify the owner of the health record. Other fields are for patients
{
“Record”:{“PatientId”:”patient1”,
“Address”: “Address yy, 123 Street, City”,
“Telephone”:17615945896,
“Diagnosis”: “Common fever”,
“Medication”:Dolo”,
“Aadarnum”:0000 2345 6666,
“ allergy”:”no” “DoctorAuthorizationList”:[“doctor1”] } }