1. Introduction

2. Background Study

2.1. Blockchain Technology Concept

Blockchain Technology was first introduced in 2008 as a tool to manage cryptocurrency and was described as a concept of the distributed ledger by S. Nakamoto [13]. In [14], Blockchain is based on the hash that uses proof of work chain. Understanding Blockchain applications in e-Health requires Information Technology (IT). Information Technology has gradually evolved and is an integral part of e-Health systems. An e-Health system has different components, and the salient part of these systems is Privacy and Security. An example of e-Health is Electronic Medical Records (EMR). In [15], EMR is described as an electronic copy of the hard copy (paper copy) of medical information that contains the patient’s treatment history. The technology and its applications, such as Blockchain, are also part and parcel of e-Health systems. In recent years, the data generated across healthcare systems have grown exponentially to account for the e-Health domain. Due to the accessibility of the Internet of Things (IoT), traditional means of communicating, transmitting, sharing, storing, and accessing healthcare information are replaced by cyberspace networking [16].

2.1.1. Types of Blockchain

There are three types of Blockchain authentication and control mechanisms: (a) public, (b) private, and (c) consortium. The public authentication is decentralized and permissionless (e.g., Bitcoin, Ethereum). For instance, Ethereum implementation is a permissionless programmable Blockchain; that is, it authorizes any user to create and execute algorithm complexity on the Ethereum platform. The consensus in Ethereum is achieved by proof of work (PoW). The “hashing” is used to validate new blocks created since PoW is based on “mining” that satisfies specific requirements [17].

On the other hand, private and consortium are restricted/controlled, and permissions are needed. Private authentication is preferred over consortium because of the Hyperledger Fabric platform. Hence, private authentication is the best fit for EMR security as it requires users to be authorized to join the platform. The ideal use cases of the Hyperledger Fabric platform for Blockchain applications are in Business-to-Business (B2B) data exchange, transaction settlement, and non-repudiation. The application of Blockchain in healthcare to secure EMRs is non-repudiation. Managing patients’ EMRs is probably the area with the highest potential growth. The EMRs contain a patient’s medical information, which includes the condition and clinical progress of a patient throughout treatment [18]. The benefits of a Blockchain-based network for EMRs are as follows: (a) records are stored in a distributed ledger, (b) no centralized owner or a hub for a hacker to corrupt or breach, and (c) data is updated [18]. In Hyperledger, the chain-code services are used to secure ways to execute smart contracts. The smart contract is a set of logically defined rules for transactions with the associated World State. World State, in this context, is a database that stores data in the form of arrays of keys assigned arbitrarily [19]. The significant challenges in Blockchain applications relating to EMRs are maintaining security and privacy.

2.1.2. Characteristics of Blockchain

Blockchain Technology has distinctive properties that make it suitable for EMR systems. These properties are:

• Decentralization: This is a peer-to-peer transaction without a centralized validation or authorization system. The access is granted to each participant with the full right to verify transactions within the network [20]. To decentralize the network, technology such as cryptographic hash, digital signature, and distributed consensus mechanisms are required for security fortification. The consensus protocol is to ensure data integrity. Therefore, decentralization enhances protection against vulnerability in the network at risk of security attacks [21].
• Immutability and Transparency: This concept means that after creating and adding the block, it cannot be altered or removed [22]. The structure of the Blockchain is formed and linked together in an orderly manner that contains transaction information.
• Auditability: Any transaction in the Blockchain network is traceable to its previous transaction. Therefore, the timestamp is incorporated in transaction validation and records [23].
• Smart Contract: This is based on certain conditions; when met, it is automatically filed and executed, such as control accesses and privileges [21].
• Security: By design, the Blockchain network uses a private or public key to access or make transactions. This is due to hashing that seals each block from a third party [10].

2.1.3. Blockchain Benefit in EMR

This section concerns the benefits of Blockchain as it relates to healthcare. The existing healthcare systems have evidence that requires Blockchain to overcome some inherent problems. The management and exchange of patients’ data is a focal point for Blockchain applications. Other applications allow healthcare data to be distributed and immutable for greater security of patient records and data integrity. Table 2.0 below shows a significant and brief summary of the benefits of Blockchain Technology.

Table 2.0. Benefits of Blockchain.

Table 2.0. Benefits of Blockchain.

Benefit	Description	References
Transparency	Due to Blockchain immutability, data cannot be deleted or altered. Blockchain is a more transparent system that stores EMR.	[23,21]
Data Integrity	Blockchain ensures data integrity so that no centralized authority is at risk of security attacks.	[21,24]
Security	EMR is sensitive data, and such Blockchain provides encryption capabilities that minimize attacks and protects vulnerability.	[23,21]
Interoperability	Decentralization helps to improve interoperability which facilitates the exchange of EMRs and grants patients’ ownership and control of their records.	[23,26]
Patient-Centered	The right of patients to access or grant access to authorized personnel in EMR systems is restored.	[21,25]

2.1.4. Limitations of Blockchain in EMR

The major limitation of Blockchain is the difficulty in maintaining privacy and security [27]. The breach of security and privacy can come from users with false identities. Therefore, Blockchain’s challenge is how to ensure anonymity. Table 2.1 summarizes the literature on Blockchain challenges and considerations in EMR Systems.

Table 2.1. Limitations of Blockchain in EMR.

Table 2.1. Limitations of Blockchain in EMR.

Literature	Challenges/Considerations	References
Blockchain Adoption: Technological, organizational, and environmental considerations	The top factors are management support, organizational readiness, and organizational size.	[28]
Blockchain Application in EMR Systems:	Requirements that impact EMR systems as it relates to Blockchain application, such as non-standardized system, decentralized storage and privacy, key management and scalability, and IoT	[29]
Blockchain application for access control management: secure data storage	The encrypted information is stored in a third party that the hub services on the Blockchain.	[30]
A Blockchain that is based on data sharing system	Miners are provided with access to aggregate and reward the data bookkeeper.	[31]
IBM report: Technical challenges that restrict Blockchain application	The major challenge is scalability. Blockchain ecosystems within corporate legacy and systems of record are challenging operations.	[26]
IBM Institute for Business Value survey: Respondents from 200 healthcare executives in 16 nations	Studies show that over half cited Blockchain’s early/immature state as an issue.	[32]
Deloitte Blockchain Technology challenges in life science and EMR System	Stakeholders engage in multiple efforts, such as healthcare organizations and health plans, standardization, cost, and regulations, to ensure commitment to Blockchain adoption.	[33]

3. Methodology

3.2. Research Questions (RQs)

The Research Questions (RQs) are formulated based on the research motivations, problem statements, and the goal of this review. Table 3.1 below summarizes the research questions (RQs).

Table 3.1. Research Questions (RQs).

Table 3.1. Research Questions (RQs).

ID	Research Questions
RQ1	How can DP be integrated into BC to enhance privacy and security in the e-Health domain (e.g., EMR)?
RQ2	What factors contribute to the DP mechanisms integration in Blockchain Technology and associated issues?
RQ3	What types of datasets and programming languages are being considered for implementation?
RQ4	What are the limitations and inherent challenges of the BT and DP applications, and how can they be solved?
Note that the above questions are narrowed to only e-Health domains

3.3. Research Strategy

The sources of the information in literature are academic publications, including conference papers, journal articles, Google scholarly books, and reports. Sources also include government agencies and reputable organizations such as IBM and AMIA. The search strategy is to find relevant works and identify applications of Blockchain and Differential Privacy and mechanisms in the e-Health domain, including the cost of privacy and challenges of the proposed solutions. According to [73] recommendation, two search strategies are primary and secondary. The primary strategy includes search terms, literature resources, and the search process, as explained below.

3.3.1. Search Terms

The search keywords used in this research are shown in Table 3.2. Online libraries, various journals, and papers are considered during the keyword search. The date filter is used to screen for current literature.

Table 3.2. Search Terms and Keywords.

Table 3.2. Search Terms and Keywords.

Numbers	Keywords
1	Review, survey, literature review, background
2	Electronic medical records, e-Health domain*, electronic health record, health information technology, patient health information
3	Blockchain Technology, Differential Privacy*, privacy, data
4	Data perturbation, Differential Privacy mechanisms
*the keyword noted while searching

3.3.2. Literature Sources

The search was conducted for papers on four different electronic databases from online libraries. During the collection process, the title, the year of publication, the journal name, the number of citations, and the link are considered. The search terms with keywords for collecting conference papers and reviewing academic journals are used to formulate conceptual building blocks. The search also covers the title and abstract as keywords. The summary of the collected search is shown in Table 3.3.

Table 3.3. Numbers of Literature Review from Online Libraries.

Table 3.3. Numbers of Literature Review from Online Libraries.

Online Libraries	Numbers of Retrieved Literature
IEEE	32
ACM	8
ScienceDirect	6
AMIA	1
Others	58
Total	105

3.3.3. Search Process

A Systematic Literature Review (SLR) examined the resources’ maturity and comprehensibility during the search. The systematic review process can be divided into two main phases.

• Phase One: Initial searching phase consists of the four online library databases. Each paper is searched separately with keywords, as shown in Table 3.3.
• Phase Two: In this phase, the search is conducted based on the references of a particular paper. By scanning the list of references for relevant papers, they are added if there is a relation to the keywords.

The search results are stored and managed in Microsoft Excel. From the phase one search, 300 papers were gathered. Ninety-eight papers were gathered from phase two of the reference search, as shown in Figure 3.1.

Figure 3.1. Search and Selection Process.

Figure 3.1. Search and Selection Process.

3.3.4. Study Selection

Research papers are selected from a different webpage. Some of the papers did not offer helpful inside knowledge concerning this research. Further filtering process was carried out. More specifically, the selection process has two phases:

• Initial selection phase: The aim is to obtain papers that offer sufficient background about this research. This section applies inclusion criteria (IC) and exclusion criteria (EC) to filter any related papers that answer the research questions. IC and EC are defined below.

o

The inclusion criteria (IC) are as follows:

• Papers published from 2008 (only a few papers published in 2005 and 2006)
• Papers published until 2022
• Papers that describe Blockchain and Differential Privacy
• Papers that describe EMR, e-Health domain
• Academic papers and journals
• Review or survey papers
• Check for duplicate publications - completed or newly released of the same study

o

The exclusion criteria (EC) are presented below:

• Papers in digital libraries that are duplicated
• News, correspondences, summaries of presentations, posters, and workshop
• Abstract of papers that are not written in the English language
• Final selection phase: This phase selects papers with the acceptable quality needed to extract information. The selection in the final phase uses study quality assessment, as explained in section 3.4 below.

The citations and references from the above papers were also reviewed, and the last step included quality assessment criteria for data extraction.

3.4. Study Quality Assessment

This section addresses how quality assessment questions (QAQs) give credit to the reviewed paper. These questions are shown in Table 3.4. The questions are used for the quality assessment of the paper and the criteria. QAQ1 evaluates how the e-Health domain uses Blockchain and Differential Privacy to enhance protection for sensitive health information. Noticeably, the researchers have used the DP method to address the security concerns in the e-Health domain. QAQ2 attempts to discover if the papers provide a more comprehensive perspective other than EMR systems since the privacy of personal information is cut across all fields. QAQ3 explores whether the research results can be deployed to real-world applications. QAQ4 evaluates common limitations in the papers that are inherent. QAQ5 identifies similarities in research questions, while QAQ6 defines different methods to provide solutions. Finally, 20 papers have been selected, as shown in Table 3.5.

Table 3.4. Quality Assessment Questions.

Table 3.4. Quality Assessment Questions.

ID	Quality Assessment Questions
QAQ1	Are the review papers related to e-Health domain under Blockchain and Differential Privacy?
QAQ2	Do the papers cover other Differential Privacy applications under different fields?
QAQ3	Do the papers use theoretical or practical based methods to answer research questions?
QAQ4	Are there common or inherent limitations in their studies?
QAQ5	Is the research question similar or different from other papers?
QAQ6	Do the proposed methods provide solutions that are different from the existing papers?

Table 3.5. List of Papers for Methodology.

Table 3.5. List of Papers for Methodology.

Category	Papers Selection*
EMR Privacy	Roehrs et al. [20], ElSalamouny et al. [43], Saleheen et al. [77], Raisaro et al. [98], Lin et al. [78], Guan et al. [101], Machanavajjhala et al. [44], [98], Alnemari et al. [93], Hadian et al. [80], Mohammed et al. [97], Tang et al. [102], Raisaro et at. [99]
Real-Time Health Data	Geo et al. [83], McSherry et al. [46], Machanavajjhala et al [45], Zhang et al. [3]
Health Survey Data Protection	Luo et al. [84], Narayanan et al. [104], Valdezet al. [91], Narayanan et al. [105]
*This selection is for the research framework and methodology

4. Results

Figure 4.1. The Taxonomy for Differential Privacy in Approach in e-Health Domains.

Figure 4.1. The Taxonomy for Differential Privacy in Approach in e-Health Domains.

4.3. RQ3: What types of datasets and programming languages are being considered for implementation?

The implementation of any proposed solution depends on the quality of the datasets. As reviewed in section 2.2.1, data perturbation mechanisms for Laplace and Gaussian use numerical datasets, while Exponential uses a non-numerical dataset. The dataset used in data perturbation mechanisms is a structured dataset. However, further research reveals that most proposal applications grouped datasets into public and private datasets based on availability.

• The EMR Privacy category, according to Saleheen et al. [77], shows a dataset with 660 hours of ECG (electrocardiogram) from participants whose private dataset was collected. Lin et al. [78] collected private datasets from wearable sensors, [79] collected heart disease datasets, and Hadian et al. [80] collected datasets from wearable devices that users attached to their bodies to monitor heart rate. A blood bank dataset containing individual information has utilized a research record dataset [81]. In addition, datasets are also obtained during activities such as walking, running, and sleeping. Kim et al. [82] obtained a dataset from daily step counts using a Gear S3 smartwatch. Table 4.1 below shows a summary of dataset utilization in the e-Health domain.
• The Real-Time Health Data category utilizes the flu dataset that Geo et al. [83] harnesses. Wearables are also used to record and share real-time health datasets. In [78], the heart rate dataset was recorded to be used in research–real-time data. The summary is shown in Table 4.1 below.
• Health Survey Data Protection discusses and provides inside surveys according to users’ perspectives. Most of these datasets from a database are statistically queried. Luo et al. [84] surveyed two real-world case studies. One of the cases uses a health survey based on students’ heart rates to find the average and distribution statistically. The second case is for collaboration to classify models based on emotions. Yang et al. [85] also use real-world public datasets with one million health datasets. The summary is shown in Table 4.1 below.

Table 4.1. Different Types of Datasets.

Table 4.1. Different Types of Datasets.

Data Type	EMR Privacy	Real-Time Health Data	Health Survey Data Protection
Private (Heart-related)	[78, 80]	[78]	[84]
Public	[81]	_	[85]
Private	[78]	_	_
Public (Activities, e.g., running, walking)	[81]	_	_
Private (Wearable sensors)	[82, 80]	[83]	_
A systemic literature review (SLR) on e-Health data under Differential Privacy

5. Challenges and Limitations

6. Contributions and Recommendations

Conclusions

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