Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review
Abstract
:1. Introduction
2. Background
3. Materials and Methods
4. Results
4.1. Mobile Health Application
4.2. IoT
4.3. Blockchain
4.4. Cloud Computing
4.5. Other Technologies
5. Discussion
5.1. Challenges and Future Directions of the Technologies Used in HISs
5.1.1. Mobile Health Applications
5.1.2. IoT
5.1.3. Blockchain
5.1.4. Cloud Computing
5.2. Secure Access Control
5.3. Secure Data Sharing
5.4. Secure Data Storage
6. Limitations
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Yusof, M.M.; Papazafeiropoulou, A.; Paul, R.J.; Stergioulas, L.K. Investigating Evaluation Frameworks for Health Information Systems. Int. J. Med. Inform. 2008, 77, 377–385. [Google Scholar] [CrossRef] [PubMed]
- Vora, J.; Italiya, P.; Tanwar, S.; Tyagi, S.; Kumar, N.; Obaidat, M.S.; Hsiao, K.F. Ensuring Privacy and Security in E-Health Records. In Proceedings of the International Conference on Computer, Information and Telecommunication Systems (CITS), Colmar, France, 11–13 July 2018. [Google Scholar]
- Mbonihankuye, S.; Nkunzimana, A.; Ndagijimana, A. Healthcare Data Security Technology: HIPAA Compliance. Wirel. Commun. Mob. Comput. 2019, 2019, 1927495. [Google Scholar] [CrossRef]
- Qayyum, A.; Qadir, J.; Bilal, M.; Al-Fuqaha, A. Secure and Robust Machine Learning for Healthcare: A Survey. IEEE Rev. Biomed. Eng. 2020, 14, 156–180. [Google Scholar] [CrossRef] [PubMed]
- Agbo, C.C.; QMahmoud, H.; Eklund, J.M. Blockchain Technology in Healthcare: A Systematic Review. Healthcare 2019, 7, 56. [Google Scholar] [CrossRef] [PubMed]
- Mohamad Jawad, H.H.; Bin Hassan, Z.; Zaidan, B.B.; Mohammed Jawad, F.H.; Mohamed Jawad, D.H.; Alredany, W.H.D. A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and Recommendations. Electronics 2022, 11, 3223. [Google Scholar] [CrossRef]
- Katarahweire, M.; Bainomugisha, E.; Mughal, K.A.; Ngubiri, J. Form-based security in mobile health data collection systems. Secur. Priv. 2021, 4, e155. [Google Scholar] [CrossRef]
- Ullah, I.; Amin, N.U.; Khan, M.A.; Khattak, H.; Kumari, S. An Efficient and Provable Secure Certificate-Based Combined Signature, Encryption and Signcryption Scheme for Internet of Things (IoT) in Mobile Health (M-Health) System. J. Med. Syst. 2020, 45, 4. [Google Scholar] [CrossRef]
- Keshta, I.; Odeh, A. Security and privacy of electronic health records: Concerns and challenges. Egypt. Inform. J. 2021, 22, 177–183. [Google Scholar] [CrossRef]
- Harman, L.B.; Flite, C.A.; Bond, K. Electronic Health Records: Privacy, Confidentiality, and Security. Am. Med. Assoc. J. Ethics 2012, 14, 712–719. [Google Scholar]
- Basil, N.N.; Solomon, A.; Chukwuyem, E.; Ekokobe, F. Health Records Database and Inherent Security Concerns: A Review of the Literature. Cureus 2022, 14, e30168. [Google Scholar] [CrossRef]
- Fathima Shah, W. Preserving Privacy and Security: A Comparative Study of Health Data Regulations—GDPR vs. HIPAA. Int. J. Res. Appl. Sci. Eng. Technol. 2023, 11. [Google Scholar] [CrossRef]
- Amato, F.; Casola, V.; Cozzolino, G.; De Benedictis, A.; Mazzocca, N.; Moscato, F. A Security and Privacy Validation Methodology for e-Health Systems. ACM Trans. Multimed. Comput. Commun. Appl. 2021, 17. [Google Scholar] [CrossRef]
- Joppi, R.; Bertele, V.; Vannini, T.; Garattini, S.; Banzi, R. Food and Drug Administration vs European Medicines Agency: Review times and clinical evidence on novel drugs at the time of approval. Br. J. Clin. Pharmacol. 2020, 86, 170–174. [Google Scholar] [CrossRef]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef]
- Simplicio, M.A.; Iwaya, L.H.; Barros, B.M.; Carvalho, T.C.; Näslund, M. SecourHealth: A Delay-Tolerant Security Framework for Mobile Health Data Collection. IEEE J. Biomed. Health Inform. 2015, 19, 761–772. [Google Scholar] [CrossRef]
- Tong, Y.; Sun, J.; Chow, S.S.; Li, P. Cloud-Assisted Mobile-Access of Health Data With Privacy and Auditability. IEEE J. Biomed. Health Inform. 2014, 18, 419–429. [Google Scholar] [CrossRef]
- Xie, Y.; Zhang, K.; Kou, H.; Mokarram, M.J. Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing. J. Cloud Comput. 2022, 11. [Google Scholar] [CrossRef] [PubMed]
- Bigini, G.; Lattanzi, E. Toward the InterPlanetary Health Layer for the Internet of Medical Things With Distributed Ledgers and Storages. IEEE Access 2022, 10, 82883–82895. [Google Scholar] [CrossRef]
- Kong, F.; Zhou, Y.; Xia, B.; Pan, L.; Zhu, L. A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment. IEEE Access 2019, 7, 161822–161830. [Google Scholar] [CrossRef]
- Agrahari, A.K.; Varma, S.; Venkatesan, S. Two factor authentication protocol for IoT based healthcare monitoring system. J. Ambient Intell. Humaniz. Comput. 2023, 14, 16081–16098. [Google Scholar] [CrossRef]
- Ullah, F.; Ullah, I.; Khan, A.; Uddin, M.I.; Alyami, H.; Alosaimi, W. Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network. J. Healthc. Eng. 2020, 2020, 8824907. [Google Scholar] [CrossRef]
- Shreya, S.; Chatterjee, K.; Singh, A. A smart secure healthcare monitoring system with Internet of Medical Things. Comput. Electr. Eng. 2022, 101, 107969. [Google Scholar] [CrossRef]
- Bashir, A.; Mir, A.H. Lightweight Secure MQTT for Mobility Enabled e-health Internet of Things. Int. Arab. J. Inf. Technol. 2021, 18, 773–781. [Google Scholar] [CrossRef]
- Ding, R.; Zhong, H.; Ma, J.; Liu, X.; Ning, J. Lightweight Privacy-Preserving Identity-Based Verifiable IoT-Based Health Storage System. IEEE Internet Things J. 2019, 6, 8393–8405. [Google Scholar] [CrossRef]
- Yongjoh, S.; So-In, C.; Kompunt, P.; Muneesawang, P.; Morien, R.I. Development of an Internet-of-Healthcare System Using Blockchain. IEEE Access 2021, 9, 113017–113031. [Google Scholar] [CrossRef]
- Ghayvat, H.; Sharma, M.; Gope, P.; Sharma, P.K. SHARIF: Solid Pod-Based Secured Healthcare Information Storage and Exchange Solution in Internet of Things. IEEE Trans. Ind. Inform. 2022, 18, 5609–5618. [Google Scholar] [CrossRef]
- Arul, R.; Al-Otaibi, Y.D.; Alnumay, W.S.; Tariq, U.; Shoaib, U.; Piran, M.J. Multi-modal secure healthcare data dissemination framework using blockchain in IoMT. Pers. Ubiquitous Comput. 2021. [Google Scholar] [CrossRef]
- Khan, A.A.; Wagan, A.A.; Laghari, A.A.; Gilal, A.R.; Aziz, I.A.; Talpur, B.A. BIoMT: A State-of-the-Art Consortium Serverless Network Architecture for Healthcare System Using Blockchain Smart Contracts. IEEE Access 2022, 10, 78887–78898. [Google Scholar] [CrossRef]
- Saini, A.; Zhu, Q.; Singh, N.; Xiang, Y.; Gao, L.; Zhang, Y. A Smart-Contract-Based Access Control Framework for Cloud Smart Healthcare System. IEEE Internet Things J. 2021, 8, 5914–5925. [Google Scholar] [CrossRef]
- Mnyawi, R.; Kombe, C.; Sam, A.; Nyambo, D. Blockchain-based Data Storage Security Architecture for e-Health Care Systems: A Case of Government of Tanzania Hospital Management Information System. Int. J. Comput. Sci. Netw. Secur. 2022, 22, 364–374. [Google Scholar]
- Xu, G.; Qi, C.; Dong, W.; Gong, L.; Liu, S.; Chen, S.; Liu, J.; Zheng, X. A Privacy-Preserving Medical Data Sharing Scheme Based on Blockchain. IEEE J. Biomed. Health Inform. 2022, 27, 698–709. [Google Scholar] [CrossRef]
- Dubovitskaya, A.; Baig, F.; Xu, Z.; Shukla, R.; Zambani, P.S.; Swaminathan, A.; Jahangir, M.M.; Chowdhry, K.; Lachhani, R.; Idnani, N.; et al. ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care. J. Med. Internet Res. 2020, 22, e13598. [Google Scholar] [CrossRef]
- Kim, H.J.; Kim, H.H.; Ku, H.; Yoo, K.D.; Lee, S.; Park, J.I.; Kim, H.J.; Kim, K.; Chung, M.K.; Lee, K.H.; et al. Smart Decentralization of Personal Health Records with Physician Apps and Helper Agents on Blockchain: Platform Design and Implementation Study. JMIR Med. Inform. 2021, 9, e26230. [Google Scholar] [CrossRef]
- Son, S.; Lee, J.; Kim, M.; Yu, S.; Das, A.K.; Park, Y. Design of Secure Authentication Protocol for Cloud-Assisted Telecare Medical Information System Using Blockchain. IEEE Access 2020, 8, 192177–192191. [Google Scholar] [CrossRef]
- Shakil, K.A.; Zareen, F.J.; Alam, M.; Jabin, S. BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud. J. King Saud Univ.-Comput. Inf. Sci. 2020, 32, 57–64. [Google Scholar] [CrossRef]
- Qiu, H.; Qiu, M.; Liu, M.; Memmi, G. Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0. IEEE J. Biomed. Health Inform. 2020, 24, 2499–2505. [Google Scholar] [CrossRef] [PubMed]
- Son, J.; Kim, J.D.; Na, H.S.; Baik, D.K. Dynamic access control model for privacy preserving personalized healthcare in cloud environment. Technol. Health Care 2015, 24 (Suppl. S1), S123–S129. [Google Scholar] [CrossRef] [PubMed]
- Khan, F.; Reyad, O. Application of intelligent multi agent based systems for E-healthcare security. Inf. Sci. Lett. 2019, 8, 67–72. [Google Scholar]
- Padinjappurathu Gopalan, S.; Chowdhary, C.L.; Iwendi, C.; Farid, M.A.; Ramasamy, L.K. An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems. Sensors 2022, 22, 5574. [Google Scholar] [CrossRef] [PubMed]
- Reyad, O.; Karar, M.E. Secure CT-Image Encryption for COVID-19 Infections Using HBBS-Based Multiple Key-Streams. Arab. J. Sci. Eng. 2021, 46, 3581–3593. [Google Scholar] [CrossRef] [PubMed]
- Salim, M.M.; Park, J.H. Federated Learning-based secure Electronic Health Record sharing scheme in Medical Informatics. IEEE J. Biomed. Health Inform. 2022, 27, 617–624. [Google Scholar] [CrossRef] [PubMed]
- Edemacu, K.; Jang, B.; Kim, J.W. Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure. IEEE J. Biomed. Health Inform. 2020, 24, 2960–2972. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Z.; Liu, W.; Ma, R.; Shirazi, S.H.; Xie, Y. Lightweight Healthcare Wireless Body Area Network Scheme With Amplified Security. IEEE Access 2021, 9, 125739–125752. [Google Scholar] [CrossRef]
- Yi, X.; Bouguettaya, A.; Georgakopoulos, D.; Song, A.; Willemson, J. Privacy Protection for Wireless Medical Sensor Data. IEEE Trans. Dependable Secur. Comput. 2016, 13, 369–380. [Google Scholar] [CrossRef]
- Zhang, M.; Chen, Y.; Susilo, W. PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems. IEEE Internet Things J. 2020, 7, 10660–10672. [Google Scholar] [CrossRef]
- Dzissah, D.A.; Lee, J.S.; Suzuki, H.; Nakamura, M.; Obi, T. Privacy Enhanced Healthcare Information Sharing System for Home-Based Care Environments. Healthc. Inform. Res. 2019, 25, 106–114. [Google Scholar] [CrossRef]
- Chatterjee, A.; Gerdes, M.W.; Khatiwada, P.; Prinz, A. SFTSDH: Applying Spring Security Framework With TSD-Based OAuth2 to Protect Microservice Architecture APIs. IEEE Access 2022, 10, 41914–41934. [Google Scholar] [CrossRef]
- Hu, J.; Liang, W.; Hosam, O.; Hsieh, M.Y.; Su, X. 5GSS: A framework for 5G-secure-smart healthcare monitoring. Connect. Sci. 2022, 34, 139–161. [Google Scholar] [CrossRef]
- Roehrs, A.; Da Costa, C.A.; da Rosa Righi, R.; De Oliveira, K.S.F. Personal Health Records: A Systematic Literature Review. J. Med. Internet Res. 2017, 19, e5876. [Google Scholar] [CrossRef]
- Mirza, A.B. Potential of Mobile Devices in New Zealand Healthcare. In Masters of Engineering in Software; Massey University: Albany, Auckland, New Zealand, May 2008. [Google Scholar]
- Dogtown Media. Data Backup and Disaster Recovery Strategies for Healthcare App Data Storage. Available online: https://www.dogtownmedia.com/data-backup-and-disaster-recovery-strategies-for-healthcare-app-data-storage/ (accessed on 12 January 2024).
- Arora, S.; Yttri, J.; Nilse, W. Privacy and Security in Mobile Health (mHealth) Research. Alcohol. Res. 2014, 36, 143–151. [Google Scholar]
- Elhoseny, M.; Thilakarathne, N.N.; Alghamdi, M.I.; Mahendran, R.K.; Gardezi, A.A.; Weerasinghe, H.; Welhenge, A. Security and Privacy Issues in Medical Internet of Things: Overview, Countermeasures, Challenges and Future Directions. Sustainability 2021, 13, 11645. [Google Scholar] [CrossRef]
- Thapa, S.; Bello, A.; Maurushat, A.; Farid, F. Security Risks and User Perception towards Adopting Wearable Internet of Medical Things. Int. J. Environ. Res. Public Health 2023, 20, 5519. [Google Scholar] [CrossRef] [PubMed]
- Tandon, R.; Cupta, P.K. Security and Privacy Challenges in Healthcare Using Internet of Things. In IoT-Based Data Analytics for the Healthcare Industry; Singh, S.K., Singh, R.S., Pandey, A.K., Udmale, S.S., Chaudhary, A., Eds.; Academic Press: London, UK, 2021; pp. 149–165. [Google Scholar]
- 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. [Google Scholar] [CrossRef] [PubMed]
- Yinka, O.T.; Haw, S.C.; Yap, T.T.V.; Subramaniam, S. Improving the data access control using blockchain for healthcare domain. F1000 Res. 2021, 10, 901. [Google Scholar] [CrossRef]
- Kiania, K.; Jameii, S.M.; Rahmani, A.M. Blockchain-based privacy and security preserving in electronic health: A systematic review. Multimed. Tools Appl. 2023, 82, 28493–28519. [Google Scholar] [CrossRef] [PubMed]
- Sanka, A.I.; Cheung, R.C.C. A systematic review of blockchain scalability: Issues, solutions, analysis and future research. J. Netw. Comput. Appl. 2021, 195, 103232. [Google Scholar] [CrossRef]
- Zhang, R.; Xue, R.; Liu, L. Security and Privacy for Healthcare Blockchains. IEEE Trans. Serv. Comput. 2022, 15, 3668–3686. [Google Scholar] [CrossRef]
- Ghosh, P.K.; Chakraborty, A.; Hasan, M.; Rashid, K.; Siddique, A.H. Blockchain Application in Healthcare Systems: A Review. Systems 2023, 11, 38. [Google Scholar] [CrossRef]
- Mehrtak, M.; SeyedAlinaghi, S.; MohsseniPour, M.; Noori, T.; Karimi, A.; Shamsabadi, A.; Heydari, M.; Barzegary, A.; Mirzapour, P.; Soleymanzadeh, M.; et al. Security challenges and solutions using healthcare cloud computing. J. Med. Life 2021, 14, 448. [Google Scholar] [CrossRef]
- AI-Issa, Y.; Ottom, M.A.; Tamrawi, A. eHealth Cloud Security Challenges: A Survey. J. Healthc. Eng. 2019, 2019, 7516035. [Google Scholar]
- Attarian, R.; Hashemi, S. An anonymity communication protocol for security and privacy of clients in IoT-based mobile health transactions. Comput. Netw. 2021, 190, 107976. [Google Scholar] [CrossRef]
Health Information System | Security Technologies | Privacy Technologies | Advantages | Disadvantages |
---|---|---|---|---|
Electronic Health Records (EHRs) | Encryption, Access Control, Auditing | Data Masking, Patient Consent Mechanisms | Improved data integrity, Efficient access control | Complex implementation, High initial setup costs, Privacy concerns, Concerns over data breaches |
Health Information Exchange (HIE) | Secure Data Transmission Protocols, Identity Management | Anonymization Techniques, Consent Management Systems | Enhanced interoperability and data sharing | Concerns over data breaches during exchange, Consent management challenges |
Clinical Trial Management Systems | Secure Data Storage, Blockchain for Auditing | De-identification Methods, Informed Consent Platforms | Enhanced traceability, Immutable data records | Limited scalability, Ethical concerns related to consent |
Database | Search Within | Result with No Constraints | Constraints | Result with Constraints | Result after Removing Duplicate Articles | Result after Removing Irrelevant Articles |
---|---|---|---|---|---|---|
Scopus | Article Title, Abstract, and Keywords | 564 | Article, English, Published from 2002 to 2022 | 247 | 129 | 17 |
Web of Science | All fields | 464 | Article, English, Published from 2002 to 2022 | 268 | 125 | 23 |
PubMed | All fields | 349 | Article, English, Published from 2002 to 2022 | 329 | 328 | 27 |
Medline | All fields | 403 | Article, English, Published from 2002 to 2022 | 375 | 78 | 22 |
IEEE | All fields | 223 | Article, English, Published from 2002 to 2022 | 54 | 54 | 27 |
Technology Used | Count, n |
---|---|
Mobile Health Application | 5 |
IoT | 7 |
Blockchain | 9 |
Cloud Computing | 5 |
Other Technologies | 10 |
Reference | Research Aim | Technology Used | Mentioned Factors | Main Findings | Empirical Evidence |
---|---|---|---|---|---|
[16] | Propose a lightweight security framework as a flexible solution for securing mobile health data collection systems, providing many security services for both stored and in-transit data | Mobile Health Application | Security Cost-effective | Tolerance to delays and lack of connectivity, Protection against device theft or loss, Secure data exchange between mobile device and server | The proposed mechanisms were integrated into an Android-based application. The experimental results show that it is possible to provide strong security for data while introducing minimal overhead to the collection process. |
[17] | The proposed system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval in emergencies and auditability for misusing health data. | Mobile Health Application | Privacy Efficiency | Build privacy into mobile health systems with the help of a private cloud | The storage and communication efficiency were analyzed. The result indicates that the proposed scheme is efficient as well as scalable. |
[8] | Propose an efficient and provable secure certificate-based combined signature, encryption, and signcryption (CBCSES) scheme | Mobile Health Application | Security (Resistant against attacks) Cost-effective | Offer the functions of both digital signature and encryption simultaneously as well as singly, Resistant against different attacks, Has better computational and communication costs | Detailed security analyses and a comparisons analysis of computational costs and communication overhead with the relevant existing schemes were carried out. The results obtained authenticate the superiority of the scheme with enhanced security. |
[18] | Adopt an effective privacy-preserving technique to guarantee the sensitive information of people is secure. The proposed method uses anomaly detection based on wearable sensors in mobile cloud computing and a hash technique. | Mobile Health Application | Privacy Cost-effective | Achieves a good balance between anomaly detection accuracy and privacy-preservation capability, Minimizes privacy disclosure concerns | Simulated experiments were enacted and deployed to prove the feasibility of the proposal in terms of anomaly detection performances including accuracy, privacy preservation, and computational time in the cloud environment. |
[7] | Facilitate the addition of security requirements into data collection processes. Propose a data sensitivity classification model in order to determine the sensitivity levels of form attributes depending on the context and sensitive parameters. | Mobile Health Application | Security | The security mitigations specified during form design are executed once the secure form is loaded on the mobile device during data collection. | Demonstrated the feasibility of this approach by implementing a prototype in an existing form designer tool for mobile health data collection |
[19] | Handle sensitive data by preserving privacy and guaranteeing data availability without relying on a third party | IoT | Privacy Data availability | Good scalability and a modest impact on the performance of the application, Stakeholder always gains access to user data and avoids a single point of failure | Real-world experiments were and tested by connecting them with a modified IoMT application. The results obtained confirmed the feasibility of the proposed solution showing good scalability and a modest impact on the performance of the application. |
[20] | Propose a security reputation model, based on a cloud environment, to protect the privacy of health data. Firstly, the text information of user health data was pre-classified by using the S-AlexNet convolutional neural network. Then, a recommendation incentive strategy based on dynamic game theory is proposed. | IoT | Security Privacy | Has reliable data recognition rate, convergence time, and recommendation efficiency, Mobile attacks are effectively resisted, The security factor of the user cloud service environment is improved | Experimental analysis on the Aliyun platform shows that the SCNN-DGT model is superior to the existing models. |
[21] | Propose a new authentication scheme where the legitimate user can register through a trusted authority, which secures against prevailing attacks and key escrow problems | IoT | Security Cost-effective | Cost-effective with improved functionality, Secure against different notable attacks in the informal security analysis | Formal security analysis of the proposed protocol was performed using the Burrows–Abadi–Needham (BAN) logic and the real or random model. The security verification was performed using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, and a detailed comparative analysis of the communication cost is also included. The results prove that the proposed protocol is more effective and efficient compared to the other schemes. |
[22] | Propose a clustering medical healthcare–IoT-based infrastructure with restricted access for privacy-aware data dissemination for wireless body area network | IoT | Privacy Cost-effective | Efficient cluster formation in minimal time, minimal information loss, and execution time for data dissemination, Increases the privacy of the patient’s data in a better way | The efficiency of the proposed algorithm was evaluated against the state-of-the-art algorithm by performing extensive simulations. The results demonstrate the benefits of the proposed algorithms. |
[23] | Develop a lightweight mutual authentication protocol for securing sensitive patient health information, Ensure the privacy of sensitive patient data while sharing with other smart community users | IoT | Security Privacy | Resists against all network attacks, Maintains patient data privacy in a multiuser scenario, Secures entity authentication to access the patient’s stored cloud-based data, Has a high data encryption rate | A formal security analysis based on Burrows–Abadi–Needham (BAN) logic and a performance comparison of the proposed scheme with existing schemes were conducted. The proposed model is capable of maintaining the patient’s data privacy, reducing service latency, and providing security from intruders through the authentication model. |
[24] | Design an energy-efficient security mechanism using lightweight messaging protocol for exchanging medical data between client nodes and using lightweight cryptographic operations to encipher the sensitive data | IoT | Security Privacy Efficiency (energy) | Facilitates the mobility of patients while maintaining security and privacy in a particular monitoring area, Being energy-efficient, provides end-to-end data confidentiality and mobility support | It uses the Cooja simulator to create and analyze an e-health system scenario. The results show that the scheme is efficient compared to existing state-of-the-art mechanisms. |
[25] | Lightweight secure health storage system preserving both the privacy and availability of patients’ health data, preventing damage to patients’ conditions from corrupted data and improving the reliability of the health storage system | IoT | Security Cost-effective | Reduces the system’s computational cost and management burden of third-party verifiers | A functionalities comparison of the proposed scheme and other schemes and an evaluation of the computational cost and communication cost through numerical analysis and simulated experiment were conducted. The results indicate that the proposed system is feasible. |
[26] | Ensure secure sharing of patient data among participating hospitals in the network, by proposing Internet-of-Healthcare Systems which provides the highest level of storage and access security possible, overcoming the security and data administration problems by using blockchain | Blockchain | Security Privacy Data confidentiality | Provides greater functionality, Addresses the security of data transmission, data processing, and secure data storage | Used real data from 157 hospitals. The system addressed the security of data transmission, data processing, and secure data storage. |
[27] | Provide a decentralized solution for data communication, combining two decentralized technologies, a solid ecosystem and blockchain technology, to tackle all potential security issues using solidity-based smart contracts | Blockchain | Security | Mitigates threats posed to data while using the traditional approach. Healthcare data are kept confidential to secure personal information and medical history, Interoperability issues were addressed by introducing a decentralized solution to the healthcare domain | Performance evaluation was conducted using Ganache, JMeter, and manual observations. Latency was proven to increase as the size of files increased and the number of users accessing resources increased. |
[28] | Design a secure system for optimized protection and privacy criteria of health data management, suggesting a secure and energy-efficient e-health system that would use IoMT to reduce energy usage and increase data provision. | Blockchain | Security Privacy | Achieves high accuracy, prediction, less delay, latency, and response time, Can authenticate each node by establishing public and private keys | Performance metrics were considered, such as the accuracy ratio, prediction ratio, response time, delay time, and latency range. The experimental results are based on the collected samples from a healthcare institution. The results show that the proposed model is effective and secure. |
[29] | Address the fundamental issues, limitations, and challenges in blockchain, Hyperledger, and the IoT, and provide a design of an efficient distributed architecture | Blockchain | Security Privacy Cost-effective | Reduces the resource constraints and increases security and privacy with secure and protected protocols for medical ledger preservation, Minimizes resource consumption throughout service delivery, | The proposed serverless e-healthcare application was evaluated and examined. The experimental results demonstrate an efficient performance of the proposed blockchain Hyperledger fabric-enabled consortium network called BIoMT. |
[30] | Build a dynamic access control framework based on a smart contract, which is built on top of a distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system | Blockchain | Security Efficient for real-time Cost-effective | The proposed access control is efficient for real-time IoT-enabled smart healthcare systems and enables all the entities to share electronic medical records (EMRs) with the permission of the patient, and it allows a new entity to be added at any time, making it more practical and dynamic. | The performance of the access mechanism was evaluated. The performance evaluation and efficiency analysis demonstrate the feasibility of the proposed scheme in a real-time smart healthcare system for a secure, decentralized, distributed, and patient-centric access control. |
[31] | Develop a system with secure data storage architecture to address cybersecurity storage challenges through private data collection to guarantee data privacy | Blockchain | Security Efficiency (performance) | Provides secured data storage, with higher overall performance, Cost-effectiveness of secured data storage and low energy consumption | The performance and cost of the architecture was evaluated. The results show that Hyperledger Fabric blockchain architecture shows higher overall performance compared to Ethereum. |
[32] | Propose a new data-sharing scheme for medical scenarios, which breaks system boundaries and realizes cross-hospital diagnosis using attribute-based encryption technology to encrypt patient medical data | Blockchain | Privacy | Efficient, correct, and well adapted in medical scenarios, Realize medical data sharing and improve the utilization of social medical resources on the premise of protecting medical privacy | Theoretical analysis and experiments of a prototype implementation were conducted. The results show that the scheme solves the contradiction between the privacy preservation of medical data and the necessity of data sharing. |
[33] | Develop a system that will facilitate secure, trustable management, sharing, and aggregation of electronic health data, ensuring patient privacy protection and security with respect to the requirements for healthcare data management, including the access control policy specified by the patient | Blockchain | Security Cost-effective | Ensures privacy, security, availability, and granular access control over highly sensitive patient data | Implementation of a prototype was used. The results demonstrate that the methodology is general and can be easily extended to support other types of patient care. |
[34] | Design and build an ecosystem that provides efficient and effective decentralized health data management and exchange operations by applying a prototype blockchain and smart contract to a patient device | Blockchain | Security Efficiency (data management) | Enabled not only at the overall personal health record or resource level but also at the granular data element and data value levels, Demonstrates that blockchain is a suitable software tool that safely and efficiently performs the required data verification and decentralized data backup processes | Three use cases were utilized, demonstrating that health data access control and authenticity verification of personal health record data were enabled not only at the overall personal health record or resource level but also at the granular data element and data value levels. |
[35] | Propose a secure authentication protocol to ensure the privacy of health data, utilizing blockchain technology to guarantee data integrity in the cloud server and applied consortium blockchain for scalability and low computational cost | Cloud computing | Security Cost-effective | Has lower computational and communication costs, Provides more security features | Informal analysis was used and computation blue and communication costs compared. The results demonstrate that the proposed protocol is efficient and has better safety compared to the related protocols. |
[36] | Present a cloud-based biometric authentication system having two different components to handle the ever-growing data of the health sector and to provide security to different users | Cloud computing | Security Efficiency (time and speed) | Has less time to run and high speed | Validation was performed through experiments and performance comparison. Experiments performed on this system revealed that it achieves a speedup of 9x which is better than other systems implemented in recent works. |
[37] | Design a user-centric data storage and sharing method to protect the safety and privacy of users’ data which could protect data safety and privacy even when both cloud servers and keys are compromised | Cloud computing | Security Privacy Efficiency (time) | Can avoid data leakage even if the keys are compromised, Has high speed | The feasibility of this system based on mobile edge computing (MEC) was evaluated in a smartphone scenario to prove the improvement in efficiency compared with standard encryption algorithms and evaluate the method with statistical and performance analysis. According to the evaluation, the proposed method is approximately 2.3 times faster than the baseline method. |
[38] | Propose a dynamic access control model for preserving data privacy, with a key feature of the proposed model being that it can deal with the healthcare domain and dynamic access control in a cloud environment | Cloud computing | Privacy | Access control can be dynamically determined by changing the context information such that even for a subject with the same role in the cloud, access permission is defined differently depending on the context information and access condition. | The article verified the ability of the proposed model to provide correct responses by representing dynamic access control through a use case scenario. The results show that the proposed model can deal with the healthcare domain and dynamic access control in a cloud environment. |
[39] | Propose a novel model based on multi-agents (user interface agent, authentication agent, connection establishment agent, and connection management agent) to maintain security and privacy while accessing the electronic health data between the users | Other Technologies | Security Privacy Efficiency (communication) | Provides effective and secure e-health security services Make ease of use and effective communication between users and the e-service providers. | The proposed model for providing security in e-health data was compared with the existing approaches. The results show that the proposed method is effective and secure. |
[40] | Provide an effective method for protecting patient privacy, utilizing log of round value-based elliptic curve cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. | Other Technologies | Security Privacy | High security and accuracy, Superior to that of the prevailing systems for disease prediction and provides better privacy and security | Performance analysis of secure data transmission and classification were conducted. The experimental outcome displays the proposed work’s performance provides better privacy and security. |
[41] | Propose a new hash-based BBS (HBBS) pseudo-random bit generator to achieve integrity and security in the transmission of medical data | Other Technologies | Security Efficiency | Has high security and good efficiency to be suitable for smart health applications and telemedicine | Multiple metrics and analyses were conducted. The proposed scheme outperforms other encryption techniques, representing a secure alternative to encrypting and decrypting medical images. |
[42] | Secure patient’s health records by maintaining user privacy and data integrity with a federated learning-based decentralized artificial intelligence model that trains data locally in hospitals and globally at research centers | Other Technologies | Security Privacy | The federated learning model performs well in accuracy, sensitivity, and specificity compared to the traditional centralized model. | The evaluation was based on the performance of the federated learning model. The results show that the scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research. |
[43] | Propose a novel access control scheme for secure sharing of health data in collaborative e-health systems, aiming to achieve immediate attribute/user revocation, collusion resistance, forward security, backward security, efficiency, and expressiveness | Other Technologies | Security Data confidentiality Efficiency | Achieves data confidentiality and fine-grained access control, Resistant to collusion attacks, Achieves both forward and backward security | The proposed scheme was simulated and a comparative analysis in relation to similar schemes was conducted. The security and performance analysis show that the proposed scheme is secure, expressive, and efficient. |
[44] | Provide a secure and lightweight healthcare wireless body area network (HWBAN) scheme by using fewer elliptic curve cryptography (ECC) operations and a physically unclonable function (PUF) to enhance security and efficiency at the same time | Other Technologies | Security Efficiency Cost-effective | Meets more security and usability requirements, Takes less computational and communication costs | The computational and communication costs were analyzed. The results show that the scheme is more practical for smart medical applications, allowing users to obtain their health status directly through their phones. |
[45] | Investigate the security and privacy issues in the medical sensor data collection and present a solution for privacy-preserving medical sensor networks | Other Technologies | Security Privacy Data confidentiality Data integrity | Achieves data confidentiality, authenticity, and integrity, Preserves patient data privacy as long as one of the three data servers is not compromised | Security and privacy analysis shows that the protocols are secure against both outside and inside attacks if one data server is not compromised. Performance analysis shows that the protocols are practical as well. |
[46] | In the proposed scheme, several secure and privacy-preserving subprotocols were designed to ensure privacy in the e-healthcare system, and then it adopted the greedy algorithm in a secure manner to perform the query and the min-heap technology to improve efficiency. | Other Technologies | Privacy Cost-effective | Practical and efficient in terms of computational cost and communication overhead. | Experiments were conducted and the performance of the scheme evaluated, in terms of the communication overhead and computational cost. The experimental results show that the scheme is applicable to different clinical scenarios. |
[47] | Develop a context-aware architecture to achieve accountability, privacy, and enhanced security in distributed home-based care systems | Other Technologies | Privacy | Enhances healthcare data access and secure information delivery to preserve user’s privacy, Enhances the workflow of users and integrates it into a seamless access control process | A prototype of the system was deployed for testing on a local network with an Android smartphone as the medical personnel terminal, confirming its feasibility. |
[48] | Develop a hybrid security solution to secure the collection and management of personal health data, providing secure hosting and operation of application services, collection, storage, processing, and provisioning of data. | Other Technologies | Security | Effectively protects the application programming interface (API) and personal health data | The technology was validated with theoretical evaluation and experimental testing, and the test results were compared with related studies qualitatively for the efficient evaluation of the implemented security solution The results show that his study can be used as a services for sensitive data (TSD) integration manual to protect personal health data in healthcare research. |
[49] | Present a framework for 5G-secure smart healthcare monitoring to achieve fast and accurate identification of context-aware health situations, a blockchain-based secure data sharing mechanism, and low-latency services for emergent patients | Other Technologies | Security Efficiency (latency and mobility) | Obtains high accuracy while significantly reducing the latency and improving the data-sharing security | A prototype system was implemented to monitor hypertensive heart disease, confirming its effectiveness with respect to a real scenario. |
Reference | Technology Name | Security and Privacy Features | Primary Functions | Advantages |
---|---|---|---|---|
[39] | Multi-agent-based systems (user interface agent, authentication agent, connection establishment agent, and connection management agent) | Security Privacy | These intelligent agents make ease of use and effective communication between patients/users and the e-service providers. | Simple and efficient access control mechanism based on the agents’ functionalities, Provides effective and secure e-health security services |
[40] | Log of round value-based elliptic curve cryptography (LR-ECC) Herding genetic algorithm-based deep learning neural network (EHGA-DLNN) | Security Privacy | Enhance the security level during data transfer after the initial authentication phase | High security and accuracy |
[41] | Hash-based BBS (HBBS) | Security | For integrity purposes, the hash value is generated using secure hash algorithm SHA-256 and is hidden in the least significant bit (LSB) of the extracted pseudo-random bits for the purpose of generating multiple keystreams. | Has high security and good efficiency |
[42] | Decentralized federated learning-based convolutional neural network | Security Privacy | Presents a privacy-friendly and secure EHR scheme for medical cyber-physical systems. | Securing valuable hospital biomedical data useful for clinical research organizations, Suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research |
[43] | Ordered binary decision diagram (OBDD) | Security | Achieves immediate attribute/user revocation, collusion resistance, forward security, backward security, efficiency, and expressiveness | The efficiency of the scheme can be attributed to the use of prime-order groups, minimized hashing operations, and reduced amount of exponentiation operations. |
[44] | Elliptic curve cryptography (ECC) operations Physically unclonable function (PUF) | Security | Improve security and efficiency at the same time, Strict formal security proof is provided to demonstrate the proposed scheme meets the security and reliability requirements | Meets more security and usability requirements and takes less computational and communication costs than related protocols proposed recently |
[45] | Lightweight encryption scheme Message authentication code (MAC) generation scheme | Security Privacy | Secures the communication between medical sensors and data servers | Achieves data confidentiality, authenticity, and integrity between each medical sensor and each data server |
[46] | Subprotocols as building blocks, such as PPC, PPCC, PPSS, and PPSU protocols | Privacy | It first designs secure and privacy-preserving several subprotocols to ensure privacy in the e-healthcare system, then it adopts the greedy algorithm in a secure manner to perform the query and the min-heap technology to improve efficiency. | Practical and efficient in terms of computational cost and communication overhead |
[47] | Near-field communication (NFC) authentication mechanism | Privacy | To generate a trustworthy source of visit records, the article uses a system that supplies concrete evidence that healthcare personnel visited a patient’s residence. | Using the NFC tag enhances the workflow of users and integrates it into a seamless access control process. It helps improve user interaction by eliminating user input tasks. |
[48] | Spring Framework services for sensitive data (TSD) Hypertext Transfer Protocol (HTTP (H)) | Security | Providing secure hosting and operation of application services, collection, storage, processing, and provisioning of data | A key element of Spring is application-level infrastructure support. It effectively protects the application programming interface (API) and personal health data. |
[49] | Edge cloud blockchain | Security | The edge cloud performs context-aware health situation identification and utilizes a blockchain-based secure data sharing mechanism to facilitate secure uploading and sharing of health data. | It identifies the health situation based on a similarity measure in the edge cloud. A blockchain-based securing data sharing mechanism is used to achieve secure sharing of health data among patients and health service providers. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shojaei, P.; Vlahu-Gjorgievska, E.; Chow, Y.-W. Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review. Computers 2024, 13, 41. https://doi.org/10.3390/computers13020041
Shojaei P, Vlahu-Gjorgievska E, Chow Y-W. Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review. Computers. 2024; 13(2):41. https://doi.org/10.3390/computers13020041
Chicago/Turabian StyleShojaei, Parisasadat, Elena Vlahu-Gjorgievska, and Yang-Wai Chow. 2024. "Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review" Computers 13, no. 2: 41. https://doi.org/10.3390/computers13020041
APA StyleShojaei, P., Vlahu-Gjorgievska, E., & Chow, Y. -W. (2024). Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review. Computers, 13(2), 41. https://doi.org/10.3390/computers13020041