Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2092 2024-02-21 09:51:34 |
2 format correct Meta information modification 2092 2024-02-21 09:56:43 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Almalawi, A.; Khan, A.I.; Alsolami, F.; Abushark, Y.B.; Alfakeeh, A.S. Healthcare Data Security for Modern Healthcare System. Encyclopedia. Available online: (accessed on 15 April 2024).
Almalawi A, Khan AI, Alsolami F, Abushark YB, Alfakeeh AS. Healthcare Data Security for Modern Healthcare System. Encyclopedia. Available at: Accessed April 15, 2024.
Almalawi, Abdulmohsen, Asif Irshad Khan, Fawaz Alsolami, Yoosef B. Abushark, Ahmed S. Alfakeeh. "Healthcare Data Security for Modern Healthcare System" Encyclopedia, (accessed April 15, 2024).
Almalawi, A., Khan, A.I., Alsolami, F., Abushark, Y.B., & Alfakeeh, A.S. (2024, February 21). Healthcare Data Security for Modern Healthcare System. In Encyclopedia.
Almalawi, Abdulmohsen, et al. "Healthcare Data Security for Modern Healthcare System." Encyclopedia. Web. 21 February, 2024.
Healthcare Data Security for Modern Healthcare System

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data.

smart hospital system internet of things security and privacy

1. Introduction

Technological advancements have made a modern method to improve human life quality possible [1]. The IoT is an innovative and developing paradigm gaining interest in several application sectors, including smart homes, smart environments, and personal and remote healthcare [2]. Research and technology researchers have identified and evaluated health data sources to learn more and solve health-related challenges [3]. Therefore, creating integrated healthcare technology can boost productivity and increase patient outcomes at every level of the medical system [4]. The world’s largest and fastest-growing industry is the healthcare sector. How healthcare is managed has changed over the past several years from a disease-centered approach to a patient-centered one [5] and a volume-based approach to a value-based strategy of healthcare delivery. The growing drive for patient-centered treatment and value-based healthcare delivery models is guided by the goals of raising public awareness of the excellence of healthcare and reducing costs [6].
By utilizing strong patient safety controls, widespread access to data, remote inpatient monitoring, quick intervention strategies, and decentralized electronic medical records, the creation of new IoT-based healthcare software applications can address some issues associated with conventional healthcare systems [7]. A system created to handle healthcare data is referred to as a medical information system (MIS). This includes the practical administration of a hospital or a system that supports the formulation of healthcare policy, as well as systems that gather, store, handle, and send a patient’s electronic medical record (EMR) [8]. These techniques can increase the quality of life for patients, boost cooperation, boost patient outcomes, lower costs, and boost the overall efficiency of e-healthcare services [9]. Systems that manage data linked to the operations of providers and healthcare groups are also included in the category of health information systems. These could be used in concert to impact research, better patient results, and improve policy and decision making. Because expenditures in extensive data analysis can be significant and create a demand for effective, affordable technology, using the cloud to study big data in healthcare stands to reason [10]. Security is a top priority because medical information systems frequently view, handle, or keep huge amounts of sensitive data. Since the equipment is typically attached to an internal network that is linked to the Internet, it is also susceptible to viruses from devices and other equipment carried into hospitals. Different kinds of malicious attacks can be caused by the attackers [11].
A form of malicious software known as ransomware stops you from reaching your device’s information, infrastructure, or networks and requests a ransom in exchange for their release. These assaults, which have been linked to issues with medical processes, disrupted patient treatment, according to more than half of ransomware victims. The probability of returning to care redirection following an assault was the greatest impact noted. If a hacker seizes control, they can instruct devices to provide false readings, deliver medication drug overdoses, or take other actions that jeopardize the health of patients [12]. Due to the substantial quantity of confidential data that healthcare organizations keep for patient treatment and activities, the sector is seen as a target-rich environment. Consequently, cybercriminals have shifted their focus from the banking industry and retail shops to healthcare facilities due to personal health information potential value being up to 50 times greater than finance data; it can be valuable to attackers. Hospitals are important infrastructure companies that keep, exchange, and use a lot of private information. To provide patients with vital medical treatment, healthcare centers also rely on a number of IoT devices and electronic medical records [13]. This particular combo appeals to cybercriminals as a prize deserving of a hefty ransom that will be rapidly paid. However, becoming a primary target involves more than just motivation and pressure. Hospitals are an excellent target for devastating malware assaults as a result of a number of current occurrences that have combined to create the perfect storm [14].
Identity theft is a major problem for cybercriminals, as it can lead to the theft of personal information such as insurance, names, policy numbers, birth dates, billing data, diagnosis codes, and bank and credit card information [15]. Fraudsters use data from healthcare organizations to create fake IDs, resell medical equipment, and file made-up claims with insurers. Many users are unaware that they have been compromised, leading to unexpected consequences and rampant medical card theft. Medical identity theft is the act of someone using confidential information such as a social security number, without permission, to make false claims to Medicare and other health insurers, which can waste government money and interfere with medical treatment [16]. These identity thefts are correlated to criminal forgery theft. The use of tools, procedures, and measures to defend against cyberattacks on networks, applications, gadgets, systems, and data is known as cyber security. Its objectives are to lower the danger of cyberattacks and safeguard against the unauthorized use of innovations, networks, and platforms [17].
Early in the COVID-19 pandemic, it was unclear how healthcare costs and use would alter globally. Although a pandemic may lead to higher health expenses, spending and use declined [18] due to other considerations. The cost of combating fraud and upholding rules is a factor. Expensive antivirus software must be obtained to shield private patient data from hackers [19]. Due to this, healthcare costs must increase to maintain patient and data security. AI and machine learning have revolutionized healthcare, particularly in medical specialties. The medical disciplines make significant use of computer systems with artificial intelligence, such as remote patient treatment, prescription transcription, enhancing doctor–patient contact, drug research and development from beginning to finish, and patient diagnosis [20]. Modern computer algorithms have recently attained accuracy levels that are comparable to those of human specialists in the field of medical sciences, despite the fact that computer systems frequently perform jobs more quickly than humans do. The goal of separating rhetoric from reality is discussing how AI is reshaping the field of medicine. AI can help healthcare organizations cut costs by deploying more sophisticated technology that is more accurate and well-suited to carry out particular functions [21]. Ensuring that the appropriate care and support are adequately suited to their health objectives might lower the number of necessary diagnostic tests and the readmission rate. It can help physicians by automatically identifying potential issues and alerting medical staff [22]. Additionally, they would lessen the likelihood of misdiagnoses and medical malpractice claims, which can add to costs.
AI applications can deal with the enormous amounts of data generated in the medical field and discover valuable knowledge that would otherwise be hidden in big medical data. Healthcare stakeholders may use AI-based computational tools to harness the power of data to review historical data, anticipate prospective outcomes, and identify the optimal actions for the current context. As a result, AI is becoming more essential to healthcare stakeholders in decision-making [23]. When putting privacy protection measures in place inside a specific system, this service represents a possible privacy breach that must be considered. End users are now more concerned than ever with the privacy of their health data due to increasing awareness among them [24]. New types of cyber-attack will be made possible by advances in AI. These attacks may use AI systems to do specific tasks more effectively than humans could or exploit flaws in AI systems that humans cannot control.
Additionally, AI systems regulate elements of malware and robot behavior that are impossible for humans to hold [25] manually. In the past, several security measures were put out to protect the transmission of patient data to hospitals [26][27][28][29]. However, the high cost and lengthy process prevent the best option from being implemented. Therefore, this research proposes a new cost-effective security algorithm for an intelligent hospital management system for COVID-19 data transmission.

2. Managing Security of Healthcare Data for a Modern Healthcare System

As new devices proliferate, they often integrate the Internet of Things (IoT), generating and exchanging a massive quantity of data in the process. As a result, providing protection in an IoT setting is more difficult than expected. Properties such as secrecy, integrity, authorization, privacy, permission, and availability must all be ensured in order to ensure security in the IoT. Following is a summary of specific recent articles related to this research: Thilagam, K. et al. [30] offered IoT-based deep learning techniques based on privacy protection and a data analytics system. The health-related data are examined in the cloud using a convolutional neural network (CNN), free of user privacy data. As a result, a safe access control component is introduced for the IoT–Healthcare system based on user attributes. Furthermore, a higher user count enables an accuracy of about 98%. Experimental research shows that the suggested solution is reliable and efficient in terms of little privacy leakage and good data integrity.
Ali, Aitizaz et al. [31] created a novel deep-learning strategy-based secure searchable blockchain that functions as a distributed database and uses homomorphic encryption to allow users to access data safely via search. Using an IoT dataset, this study evaluated and compared the recommended access control mechanisms to reference models. The hyper ledger tool’s smart contracts implement the suggested algorithms.
Deep learning (DL) methods were combined with authorized blockchain and intelligent contracts by Kumar, Randhir et al. [32] to create the unique, safe, and effective data-sharing model PBDL. To be more precise, PBDL has a blockchain-based system to register, authenticate (using zero-knowledge evidence), and verify the communicating parties before employing an innovative contract-based agreement method. The healthcare data are encoded or transformed into a new format using stacked sparse variational autoencoding (SSVAE) in this technique. In addition, the attack detection mechanism is identified and enhanced using self-attention linked bidirectional long short-term memory (SA-BiLSTM).
Kute, Shruti Suhas et al. [33] provided a study of cutting-edge research involving the IoT in healthcare, particularly on obesity, overweight, and persistent degenerative illnesses. Secrecy, integrity, authentication, access, trust, validation, information management, and storage and availability issues must be resolved for IoT in real-world applications. A description of the security, privacy, and trust problems in IoT-based machine learning depending on healthcare systems is also provided in this study.
Using a hybrid deep neural network system, Ali, Aitizaz et al. [34] proposed a new group theory (GT) that depended on the binary spring search (BSS) technique. The blockchain was presented as a distributed database to guarantee secure tracking and keyword-based access to the dataset. The proposed methodology also offered a secure critical revocation method, and various policies were updated accordingly. The security of patient healthcare information access systems incorporating blockchain and a confidence chain addressed the efficiency and safety difficulties in the existing schemes for exchanging both forms of digital healthcare data.
One such IoT and cloud computing application was the topic of a study by Anuradha, M. et al. [35]. This work’s primary goal was to develop a cancer prediction system utilizing the Internet of Things after extracting the specifics of blood results to determine whether they were normal or abnormal. Additionally, the blood results of cancer patients were encrypted and stored in the cloud for easy Internet access by doctors and nurses who needed to handle patient data discreetly. This focused on improving the calculations and processing in the healthcare industry. To offer authentication and security when dealing with patients with cancer, encryption and decryption were performed using the AES method.
Initial emphasis was placed on the fundamental security requirements for a Body Sensor Network (BSN)-based contemporary healthcare system. As a result, BSN-Care was proposed, a successful IoT-based healthcare system that enabled BSN to effectively meet these requirements Satyanarayan et al. [36].
The Authentication, Authorization, and Audit Logs (AAA) services were achieved by FBASHI, a system built on blockchain technology and fuzzy logic Zulkifl, Z. and Khan et al. [37]. This work provided a heuristic method for conducting driven flexible security, offering AAA services for medical care IoTs and networks based on the blockchain. It also suggested an approach for action driven flexible security using fuzzy logic.
For IoT-enabled hospitals, a reciprocal authentication method that protects privacy was suggested by Das, S. and Namasudra in order to accomplish quick and efficient network device verification [38]. This suggested authentication method was built using lightweight cryptographic primitives, such as XOR, combination, and hash operation, to accommodate the computing power of the IoT devices. The suggested strategy could block unwanted devices from accessing healthcare networks by establishing a safe connection between an approved device and a gateway.


  1. Land, K.J.; Boeras, D.I.; Chen, X.-S.; Ramsay, A.R.; Peeling, R.W. REASSURED diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes. Nat. Microbiol. 2019, 4, 46–54.
  2. Marques, G.; Pitarma, R.M.; Garcia, N.; Pombo, N. Internet of things architectures, technologies, applications, challenges, and future directions for enhanced living environments and healthcare systems: A review. Electronics 2019, 8, 1081.
  3. Shafique, K.; Khawaja, B.A.; Sabir, F.; Qazi, S.; Mustaqim, M. Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access 2020, 8, 23022–23040.
  4. Mansour, R.F.; El Amraoui, A.; Nouaouri, I.; Diaz, V.G.; Gupta, D.; Kumar, S. Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems. IEEE Access 2021, 9, 45137–45146.
  5. Zeadally, S.; Siddiqui, F.; Baig, Z.; Ibrahim, A. Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics. PSU Res. Rev. 2020, 4, 149–168.
  6. Zhu, H.; Wu, C.K.; Koo, C.H.; Tsang, Y.T.; Liu, Y.; Chi, H.R.; Tsang, K.-F. Smart healthcare in the era of internet-of-things. IEEE Consum. Electron. Mag. 2019, 8, 26–30.
  7. Chen, H.; Khan, S.; Kou, B.; Nazir, S.; Liu, W.; Hussain, A. A smart machine learning model for the detection of brain hemorrhage diagnosis based internet of things in smart cities. Complexity 2020, 2020, 3047869.
  8. Ennafiri, M.; Mazri, T. Internet of things for smart healthcare: A review on a potential IOT based system and technologies to control COVID-19 pandemic. In Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications; Springer International Publishing: Cham, Switzerland, 2021.
  9. Malikov, M.R.; Rustamov, A.A.; Ne’matov, N.I. Strategies for Development of Medical Information Systems. Theor. Appl. Sci. 2020, 89, 388–392.
  10. Kelly, J.T.; Campbell, K.L.; Gong, E.; Scuffham, P. The Internet of Things: Impact and implications for health care delivery. J. Med. Internet Res. 2020, 22, e20135.
  11. Javaid, M.; Khan, I.H. Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. J. Oral Biol. Craniofacial Res. 2021, 11, 209–214.
  12. Serna, S. The Increase of Ransomware Attacks within the Healthcare and Education Sector. Ph.D. Thesis, Utica University, Utica, NY, USA, 2022.
  13. Buzdugan, A. Integration of cyber security in healthcare equipment. In Proceedings of the 4th International Conference on Nanotechnologies and Biomedical Engineering: Proceedings of ICNBME-2019, Chisinau, Moldova, 18–21 September 2019; Springer International Publishing: Cham, Switzerland, 2020; pp. 681–684.
  14. Scott, C.R. Comparing Cybercrime in Banking and Healthcare Sectors. Ph.D. Thesis, Utica University, Utica, NY, USA, 2022.
  15. Richardson, R.; North, M.M.; Garofalo, D. Ransomware: The landscape is shifting-a concise report. Int. Manag. Rev. 2021, 17, 5–86.
  16. Minnaar, A.; Herbig, F.J. Cyberattacks and the cybercrime threat of ransomware to hospitals and healthcare services during the COVID-19 pandemic. Acta Criminol. Afr. J. Criminol. Vict. 2021, 34, 155–185.
  17. Ma, K.W.F.; McKinnon, T. COVID-19 and cyber fraud: Emerging threats during the pandemic. J. Financ. Crime 2022, 29, 433–446.
  18. Alam, T.; Benaida, M. Internet of things and blockchain-based framework for Coronavirus (COVID-19) disease. Int. J. Online Biomed. Eng. 2022, 18, 82–94.
  19. Mukati, N.; Namdev, N.; Dilip, R.; Hemalatha, N.; Dhiman, V.; Sahu, B. Healthcare assistance to COVID-19 patient using internet of things (IoT) enabled technologies. Mater. Today Proc. 2021, in press.
  20. Zhang, C.; Lu, Y. Study on artificial intelligence: The state of the art and future prospects. J. Ind. Inf. Integr. 2021, 23, 100224.
  21. Greco, L.; Percannella, G.; Ritrovato, P.; Tortorella, F.; Vento, M. Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognit. Lett. 2020, 135, 346–353.
  22. Hameed, K.; Bajwa, I.S.; Ramzan, S.; Anwar, W.; Khan, A. An intelligent IoT based healthcare system using fuzzy neural networks. Sci. Program. 2020, 2020, 8836927.
  23. Secinaro, S.; Calandra, D.; Secinaro, A.; Muthurangu, V.; Biancone, P. The role of artificial intelligence in healthcare: A structured literature review. BMC Med. Inform. Decis. Mak. 2021, 21, 125.
  24. Awotunde, J.B.; Misra, S. Feature extraction and artificial intelligence-based intrusion detection model for a secure internet of things networks. In Illumination of Artificial Intelligence in Cybersecurity and Forensics; Springer International Publishing: Cham, Switzerland, 2022; pp. 21–44.
  25. Puri, V.; Kataria, A.; Sharma, V. Artificial intelligence-powered decentralized framework for Internet of Things in Healthcare 4.0. Trans. Emerg. Telecommun. Technol. 2021, e4245.
  26. Othman, S.B.; Almalki, F.A.; Chakraborty, C.; Sakli, H. Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies. Comput. Electr. Eng. 2022, 101, 108025.
  27. Rawat, R.; Mahor, V.; Garg, B.; Chouhan, M.; Pachlasiya, K.; Telang, S. Modeling of cyber threat analysis and vulnerability in IoT-based healthcare systems during COVID. In Lessons from COVID-19; Academic Press: Cambridge, MA, USA, 2022; pp. 405–425.
  28. Sarosh, P.; Parah, S.A.; Bhat, G.M. An efficient image encryption scheme for healthcare applications. Multimed. Tools Appl. 2022, 81, 7253–7270.
  29. Rani, S.; Chauhan, M.; Kataria, A.; Khang, A. IoT equipped intelligent distributed framework for smart healthcare systems. arXiv 2022, arXiv:2110.04997.
  30. Thilagam, K.; Beno, A.; Lakshmi, M.V.; Wilfred, C.B.; George, S.M.; Karthikeyan, M.; Peroumal, V.; Ramesh, C.; Karunakaran, P. Secure IoT Healthcare Architecture with Deep Learning-Based Access Control System. J. Nanomater. 2022, 2022, 2638613.
  31. Ali, A.; Pasha, M.F.; Ali, J.; Fang, O.H.; Masud, M.; Jurcut, A.D.; Alzain, M.A. Deep learning based homomorphic secure search-able encryption for keyword search in blockchain healthcare system: A novel approach to cryptography. Sensors 2022, 22, 528.
  32. Kumar, R.; Kumar, P.; Tripathi, R.; Gupta, G.P.; Islam, A.K.M.N.; Shorfuzzaman, M. Permissioned Blockchain and Deep Learning for Secure and Efficient Data Sharing in Industrial Healthcare Systems. IEEE Trans. Ind. Inform. 2022, 18, 8065–8073.
  33. Kute, S.S.; Tyagi, A.K.; Aswathy, S.U. Security, privacy and trust issues in internet of things and machine learning based e-healthcare. In Intelligent Interactive Multimedia Systems for E-Healthcare Applications; Springer: Singapore, 2022; pp. 291–317.
  34. Ali, A.; Almaiah, M.A.; Hajjej, F.; Pasha, M.F.; Fang, O.H.; Khan, R.; Teo, J.; Zakarya, M. An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network. Sensors 2022, 22, 572.
  35. Anuradha, M.; Jayasankar, T.; Prakash, N.; Sikkandar, M.Y.; Hemalakshmi, G.; Bharatiraja, C.; Britto, A.S.F. IoT enabled cancer prediction system to enhance the authentication and security using cloud computing. Microprocess. Microsyst. 2021, 80, 103301.
  36. Satyanarayana, T.V.V.; Roopa, Y.M.; Maheswari, M.; Patil, M.B.; Tamrakar, A.K.; Shankar, B.P. A secured IoT-based model for human health through sensor data. Meas. Sens. 2022, 24, 100516.
  37. Zulkifl, Z.; Khan, F.; Tahir, S.; Afzal, M.; Iqbal, W.; Rehman, A.; Saeed, S.; Almuhaideb, A.M. FBASHI: Fuzzy and Blockchain-Based Adaptive Security for Healthcare IoTs. IEEE Access 2022, 10, 15644–15656.
  38. Das, S.; Namasudra, S. Lightweight and efficient privacy-preserving mutual authentication scheme to secure Internet of Things-based smart healthcare. Trans. Emerg. Telecommun. Technol. 2023, e4716.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , , ,
View Times: 60
Revisions: 2 times (View History)
Update Date: 21 Feb 2024