A Novel Information Security Framework for Securing Big Data in Healthcare Environment Using Blockchain ^†

Abstract

The Blockchain-based information security framework for health care big data environments is a framework designed for the secure storage, access, and transmission of health care data in big data environments. It combines the privacy and security advantages of encryption and decentralized networks offered by Blockchain technology with the scalability of distributed systems to provide an effective secure platform for big data applications. The framework is based on the principles of confidentiality and immutability to ensure the security and privacy of health care data. The framework is designed to support a wide range of information sources and use cases including patient records, clinical research, medical imaging, genomic data, and pharmaceutical trials. It is also designed to be compatible with existing distributed computing and data querying technologies such as Hadoop and Spark, which will help organizations to improve the accessibility of health care data. The Blockchain-based framework will also provide an audit trail, allowing hospitals and other organizations to better monitor and control access to their data. This will enable organizations to ensure compliance with HIPAA and other regulations, while providing enhanced confidentiality and privacy to users and patients.

1. Introduction

Healthcare organizations must invest in up-to-date digital security measures to protect data and systems from malicious attackers, and must have comprehensive incident response processes in place in the event of a security incident [1]. Security measures should include network segmentation, encryption, and authentication, as well as the use of cutting-edge data analytics and monitoring tools to detect suspicious behavior [2]. The information security is of the utmost importance for health care organizations operating in a big data environment. Protecting against potential attackers requires investing in effective digital security technologies and processes, staying up to date on industry best practices, and addressing legal and governmental measures [3]. As technology evolves, the healthcare industry is facing challenges with protecting patient data and ensuring patient safety [4]. Cloud computing makes it easier to store, manage, and use data securely. Furthermore, healthcare organizations can take advantage of a range of services such as authentication, encryption, and access control to protect data from unauthorized access and manipulation [5]. Combined with secure document sharing and authentication technologies, healthcare organizations can gain better control over that have access to patient data. Furthermore, machine learning algorithms are being developed to detect malicious and fraudulent behavior in the healthcare sector. The healthcare industry is advancing quickly, and organizations must adapt to these changes in order to protect patient data securely. Adapting to the latest security measures and leveraging technology such as cloud computing and machine learning are essential for protecting data and ensuring patient safety. With the right strategies in place, healthcare organizations can ensure the safety and security of patient data in the big data environment [6]. The main contributions of this research include the following,

• Ensuring confidentiality of sensitive patient data: information security is essential for preserving the privacy of protected health information (PHI) and maintaining patient trust.
• Safeguarding data integrity: information security protects the integrity of healthcare data by guarding against unauthorized access, modification, or destruction.
• Detecting and preventing breaches: information security systems monitor for unusual activity and alert administrators to potential intrusions into the system.
• Establishing data retention and purge strategies: when data is no longer needed, it should be securely deleted or purged to protect the privacy of the data [7,8,9].

2. Materials and Methods

The use of big data in healthcare is of increasing importance. It allows for better storage and analysis of healthcare data, personalized treatments, and predictions between diseases and treatments. However, the use of this information must be performed with strict security so that patient data remains confidential and secure [10]. The security of information systems is crucial to the success of any data-driven healthcare system. Security requires a multi-faceted approach that combines technical, administrative, and physical security measures to protect patient data. This includes a comprehensive security policy for data storage, encrypted transmission, secure data access, and data-handling practices [11]. In the current big data environment, where vast amounts of personal data and health data is exchanged and stored electronically, there is an increased risk of confidential patient data being breached or misused. With the emergence of cloud computing, also comes the potential for violations, data breaches, or data theft. Security breaches can be costly and damaging to a health care provider’s reputation [12]. Because of the serious consequences, it is essential for health care providers to have robust information security measures in place to protect the data they store and manage. Medical records often contain a patient’s personal data, medical information, insurance information, and more. Therefore, additional steps must be taken to protect and control access to these records. This involves protecting patient data so that malicious actors do not have access to it [13]. Health care providers must also take additional steps to ensure that patient data is kept secure from unauthorized access, either by monitoring data at rest and in transit, or by ensuring their compliance with detailed data privacy regulations [14,15,16,17]. This includes encryption, access control, authentication, data privacy regulations, and training personnel on patient data handling. It is not only essential to keep patients’ information secure, but also to increase their trust in the health care system.

Data migration is the process of transferring data from one system to another. Challenges that arise during data migration and compatibility include:

• Data volatility: Data migration can be hindered by unexpected data volatility. This is when data are changing or mutating in the source system, making it difficult to ensure a successful transfer of all data. This can be caused by human error or changes to the source system that are not communicated prior to the data migration.
• Data silos: Legacy systems often contain multiple disconnected or isolated data silos, meaning that full data integration and big-picture analysis is difficult to achieve. Data mapping and cleansing is often needed to successfully transfer data between silos.
• Data quality: Legacy systems may contain data that are inaccurate, incomplete or out of date. Data quality checks on the source system are needed to ensure that the data are usable in the new system, in addition to data cleansing and transformation activities.
• Legacy software and hardware: Sometimes it is difficult to map data from the existing system as it is outdated or in an uncommon format. This may require custom coding to successfully integrate data from the source system to the target system.
• Security and regulatory challenges: Data protection must be considered throughout the data migration process. Data security at rest and in-flight must be ensured, whether moving data within the same organization, across systems or through the cloud.

The novelty of the Blockchain-based information security framework for health care big data environment lies in its capability to provide a secure platform for the exchange of sensitive medical information. With the help of distributed-ledger technology and digital signatures, the framework ensures that no malicious actor can alter or tamper with the stored data. Additionally, it provides an immutable and encrypted record of all transactions, making it extremely difficult for outsiders to gain access to private medical records [18,19,20].

• Data tampering: Blockchain technology enables the secure and immutable storage of healthcare records, preventing malicious actors from tampering with data on the blockchain.
• Data breaches: As opposed to traditional methods of storing healthcare data, blockchain’s distributed ledger system ensures that there are no single points of failure for malicious actors to exploit, preventing large-scale data breaches.
• Data privacy: With Blockchain, healthcare organizations and patients can transmit data with complete privacy, as data is encrypted and stored securely within the network, using secure protocols such as encryption and hashing.
• Access control: Blockchain enables the implementation of effective access controls, providing healthcare organizations and patients with the ability to control who can access their data, limiting unauthorized access.
• Authentication: The Blockchain platform provides the necessary infrastructure to verify that the users accessing the system are who they say they are, preventing identity theft and ensuring the data is only used by legitimate users.

Moreover, harnessing the power of smart contracts, the framework can also set up data access conditions to ensure that only authorized entities can access the medical data when necessary.

2.1. Proposed Model

Data access control would be enforced using Blockchain-based smart contracts which would require access authorization from the appropriate parties.

$$q_p=\sqrt{2*q*R_p}$$

(1)

Blockchain technology could be combined with machine learning algorithms to enhance data security by identifying anomalies in the data and ensuring that only authorized transactions are executed.

$$v″(u)=e^v*\underset{u\to 0}{\lim }\frac{u}{\ln (u+1)}$$

(2)

The implementation of a Blockchain-based information security framework for healthcare big data could also provide a solution to tracking and auditing the usage and access of patient data over time. Blockchain technology offers an innovative and secure way to protect the health care big data environment.

$$v″(u)=e^v*\underset{u\to 0}{\lim }\frac{1}{\frac{1}{u}\ln (u+1)}$$

(3)

$$V″(u)=e^u*\underset{u\to 0}{\lim }\frac{1}{\ln {(u+1)}^{\frac{1}{u}}}$$

(4)

$$v″(u)=e^u*\frac{1}{\ln *\underset{u\to 0}{\lim }{(u+1)}^{\frac{1}{u}}}$$

(5)

$$v″(u)=e^u*\frac{1}{\ln u}$$

(6)

A Blockchain-based information security framework for the health care big data environment can provide a secure and transparent framework for protecting health care data.

$$d{v}_1=-dv+{\displaystyle \sum _{u=1}d{V}_u=0\Rightarrow \frac{d{V}_u}{d{U}_v}=1}$$

(7)

The functional block diagram has shown in the following Figure 1. The framework can provide high levels of security for data by incorporating distributed ledger technology, authentication protocols, encryption protocols, and privacy preserving technologies.

2.2. Operating Principle

Blockchain-based information security frameworks for healthcare big data environments enable secure, permission access to data records within a healthcare organization.

$$d{V}_2=U+{\displaystyle \sum _{u=1}{\omega }_u*V_u=0\Rightarrow \frac{d{V}_2}{d{U}_u}=1}$$

(8)

In short, a Blockchain-based information security framework for healthcare big data environment works by using decentralized technology and smart contracts to ensure data security.

The healthcare organizations can customize access permissions across different user groups using the framework, further securing the data.

$$\frac{d\ln ({\omega }_v)}{d{u}_v}+{\psi }_1\frac{d{v}_1}{d{u}_v}+{\psi }_2\frac{d{v}_2}{d{u}_v}=0$$

(9)

$$\ln (V_u)-\ln (U_v)+{\psi }_1-{\psi }_2{U}_v=0$$

(10)

• Establish connections with existing healthcare IT systems. Verify that the existing IT systems are compatible with the new system and that they will be able to interact with each other. Make any modifications necessary to ensure compatibility.
• Create a transition plan for transitioning from the existing IT systems to the new system. Outline all necessary steps, such as data migration, user training, and system testing, and develop a timeline for completing each step.
• Develop a strategy for data migration. Create scripts to ensure that data is accurately and securely transferred from the existing systems to the new system, making any necessary modifications along the way to ensure data accuracy.
• Train users on the new system. Develop comprehensive user training materials and conduct regularly scheduled training sessions to ensure that all users are familiar with the new system and how to use it.
• Test and debug the new system. Run rigorous tests to detect any potential problems or bugs and develop a plan to fix them.
• Finalize data migration. Make any necessary adjustments to the data migration scripts and ensure that all data has been successfully transferred to the new system.

The framework typically involves a private permission Blockchain or Distributed Ledger Technology (DLT) that readily enables the authentication, authorization, and access control of health data. Cryptographic methods help to ensure the confidentiality, immutability, and security of healthcare data through the use of encryption and hashing algorithms. Encryption is the process of encoding data so that it is unintelligible to anyone who does not have the key. By encrypting the data, it is protected from unauthorized access or manipulation. Hashing algorithms are used to create a unique identifier (hash) for data, which prevents malicious actors from being able to change the data and also prevents them from accessing the contents of the data. As a result, data is protected from manipulation and access. Both encryption and hashing algorithms help to ensure that healthcare data is protected from unauthorized access, manipulation, and loss, thereby providing a secure framework for the management of healthcare data. Additionally, using these cryptographic methods helps to ensure that patient data is immutable and cannot be altered or tampered with in any way, thus providing a high level of data security.

• Clinical trials: Clinical trials are extremely complex and depend on the secure transfer of data between patients, researchers, labs, and other stakeholders. Blockchain technology could provide a secure platform for researchers to securely connect to and exchange relevant medical data. This could enable faster recruitment, reduce errors, and ensure the data is securely stored.
• Electronic medical records (EMRs): EMRs are an important tool for promoting patient data accuracy, privacy, and security. The Blockchain could be used to ensure data security and immutability, as well as provide better patient authentication methods. With improved authentication, the Blockchain could also help to reduce medical identity fraud.
• Drug supply chain: The Blockchain could be used to improve the security, transparency, and efficiency of the drug supply chain. By providing a secure, decentralized platform for tracking medical supplies, the Blockchain could ensure drug data is immutable, reliable, and up-to-date.
• Medical insurance: The Blockchain could be used to securely store and transfer medical insurance information. This could eliminate paperwork and improve the accuracy and security of insurance transactions. The Blockchain could also be used to facilitate secure online payments for medical services and reduce overall costs.
• Medical billing: The Blockchain could be used to securely store and share patient data across multiple billing providers. This could reduce errors, increase accuracy, and ensure payments are accurate and on time.

3. Results and Discussion

The proposed Blockchain-Based Information Security Framework (BISF) has compared with the existing Big Data Security Framework (BDSF), IoT-Based Big Data Ecosystem (IBDE), Blockchain-Based AI/ML-Enabled Big Data Analytics (AIBDA) and Artificially Intelligent Switching Framework (AISF). The framework ensures adherence to regulations like HIPAA by enforcing data access permissions on a user and object-level. This means that each user is subject to specific permissions defined by the organization that can limit their access to only what they need in order to do their job. Additionally, the framework also utilizes Role-Based Access Control (RBAC) to further define and limit user access to data. This ensures that only the necessary personnel have access to any sensitive health and medical information, and that all other users are limited in the data they can access. Additionally, the framework also employs encryption and other security measures to ensure that any sensitive data is kept safe and only accessible by those with permission to view it.

3.1. Security Management

Blockchain provides an immutable record that can be used to track the sharing of data and the individuals or organizations that are accessing it. The consensus mechanism works by having miners compete to find a cryptographic hash of a block of transaction data, with the miner who finds a valid hash first being rewarded with a block reward and getting to extend the Blockchain. This incentivizes miners to put their computational power to use and increases overall network performance, making it faster and more secure. It requires miners to solve complex mathematical problems; it helps to secure the network by making any malicious attack costly and time-consuming. The scalability of the framework depends on the type of underlying Blockchain technology being used. Generally speaking, the consensus algorithms used in Blockchain systems can enable faster transaction speeds and low latency. For example, the hyper ledger Fabric platform is a highly scalable Blockchain platform that can process up to thousands of transactions per second. However, other Blockchain platforms such as Ethereum may suffer from lower transaction speeds and more latency due to its consensus mechanism.

Figure 2 shows the comparison of security management. In a computation tip, the proposed BISF reached 86.27% security management. The existing BDSF obtained 75.08%. IBDE reached 50.94%, AIBDA obtained 66.46%, and AISF reached 55.72% security management.

3.2. Network Management

Blockchain is a distributed ledger technology that enables permission data sharing and secure data storage by avoiding the need for a centralized trust authority.

Figure 3 shows the comparison of network management. In a computation tip, the proposed BISF reached 94.16% network management. The existing BDSF obtained 77.81%. IBDE reached 60.21%, AIBDA obtained 78.19% and AISF reached 68.31% network management.

3.3. Attacks Management

Blockchain-based information security in health care big data environment is a relatively new approach to mitigating the risk of cyber-attacks. It supports distributed storage and verification of medical data to ensure the integrity of health care information.

Figure 4 shows the comparison of attacks management. In a computation tip, the proposed BISF reached 90.58% attacks management. The existing BDSF obtained 74.83%. IBDE reached 56.27%, AIBDA obtained 73.35% and AISF reached 63.91% attacks management. Beyond the choice of the underlying platform, other factors such as network size, transaction volume, and network congestion can also affect the scalability of the framework. The most fundamental advantage of a Blockchain-based security system is its decentralized control. Rather than relying on a single centralized source of authority and control, a Blockchain-based system allows multiple parties to collectively manage security. This is enabled by the use of distributed ledgers, which provide a tamper-proof audit trail of security-related transactions. In comparison to traditional security approaches, a Blockchain-based security system provides greater resistance to malicious actors. Because of its distributed control, it is extremely difficult to alter or delete data stored on the Blockchain without detection. It also eliminates the need for a centralized authority to authorize or monitor transactions, further improving the system’s security. A Blockchain-based system offers more transparency, as its distributed ledger provides a clear record of all actions taken by users. This makes it easier to identify any suspicious activity and to review access privileges and permissions. Finally, this technology also simplifies the process of authentication, as the distributed ledger makes it possible to verify the legitimacy of any given user.

4. Conclusions

Security management is a comprehensive approach to managing security risks in a Blockchain-based information security environment in health care big data. The conclusion of security management requires a good understanding of the risks associated with Blockchain-based information security solutions and the development of the right security strategies to protect the critical data, systems and processes that support healthcare services. The capabilities of the framework provide long-term value in terms of longevity, interoperability, and enhancements. For instance, it will enable healthcare systems to extract data from different sources– electronic medical records, images, and sensor data, and make it available in a unified format—contributing to patient-centered care. Additionally, the framework enables patient records to be pushed to entities that can ameliorate decisions about care, and will enable the roll-out of population health measures related to COVID-19 contact tracing, crucial vital sign monitoring, and other digital health strategies, ensuring flexible and seamless transitions between different healthcare systems. In time, this will help create an ecosystem of interoperability that ultimately improves the performance of the healthcare system, increases efficiency, and reduces costs.