Securing smart health in smart cities: Blockchain technology to secure electronic health data sharing

Abstract

In the era of smart cities, safeguarding electronic health records (EHRs) is crucial to ensure the privacy and security of citizens’ sensitive medical information. Existing medical data transfer methods are vulnerable to privacy breaches, making it challenging to protect patient data. This research proposes a novel blockchain-based approach to secure EHR sharing in smart cities. Our method leverages improved association rule mining to identify sensitive information, which is then encrypted using the Siberian Tiger Integrated Tuna Swarm algorithm to generate an optimal encryption key. The encrypted data are stored on a blockchain, ensuring its integrity and confidentiality. Our proposed model demonstrates maximum robustness against various attacks, including chosen ciphertext attack, chosen-plaintext attack, known ciphertext attack, and known-plaintext attack. This research contributes to the development of secure and privacy-preserving smart health infrastructure in smart cities, enabling the safe sharing of EHRs and promoting better health-care outcomes.

Keywords

Medical data / Improved association rule mining / Blockchain / Optimal key / Siberian tiger integrated tuna swarm algorithm optimization

Funding

None.

Conflict of interest

The authors declare no conflicts of interest.

References

[1] Zhao J, Wang W, Wang D, Wang X, Mu X. PMHE: A wearable medical sensor assisted framework for health care based on blockchain and privacy computing. J Cloud Comput. 2022; 11:96. doi: 10.1186/s13677-022-00373-8

[2] Jayaram R, Prabakaran S. Onboard disease prediction and rehabilitation monitoring on secure edge-cloud integrated privacy preserving healthcare system. Egypt Inform J. 2021; 22(4):401-410. doi: 10.1016/j.eij.2020.12.003

[3] El-Samad W, Atieh M, Adda M. Transforming health insurance claims adjudication with blockchain-based solutions. Proced Comput Sci. 2023; 224:147-154. doi: 10.1016/j.procs.2023.09.022

[4] Wang G, Nurcahyo A. Designing personalized integrated healthcare monitoring system through blockchain and IoT. Proced Comput Sci. 2023; 25:223-232. doi: 10.1016/j.procs.2023.10.520

[5] Chondrogiannis E, Andronikou V, Karanastasis E, Litke A, Varvarigou T. Using blockchain and semantic web technologies for the implementation of smart contracts between individuals and health insurance organizations. Blockchain Res Appl. 2022; 2:100049. doi: 10.1016/j.bcra.2021.100049

[6] Uppal S, Kansekar B, Mini S, Tosh D. HealthDote: A blockchain-based model for continuous health monitoring using interplanetary file system. Healthc Anal. 2023; 3:100175. doi: 10.1016/j.health.2023.100175

[7] Barbaria S, Mahjoubi H, Boussi rahmouni H. A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case. Proced Comput Sci. 2023; 219:1436-1443. doi: 10.1016/j.procs.2023.01.433

[8] Mubarakali A, Bose SC, Srinivasan K, Elsir A, Elsier O. Design a secure and efficient health record transaction utilizing block chain (SEHRTB) algorithm for health record transaction in block chain. J Ambient Intell Humaniz Comput. 2019; 15:59. doi: 10.1007/s12652-019-01420-0

[9] Omar AA, Bhuiyan MZA, Basu A, Kiyomoto S, Rahman MS. Privacy-friendly platform for healthcare data in cloud based on blockchain environment. Future Gener Comput Syst. 2019; 95:511-521. doi: 10.1016/j.future.2018.12.044

[10] Huang H, Zhu P, Xiao F, Sun X, Huang Q. A blockchain-based scheme for privacy-preserving and secure sharing of medical data. Comput Secur. 2020; 99:102010. doi: 10.1016/j.cose.2020.102010

[11] Kuo TT, Kim J, Gabriel RA. Privacy-preserving model learning on a blockchain network-of-networks. J Am Med Inform Assoc. 2020; 27(3):343-354. doi: 10.1093/jamia/ocz214

[12] Amponsah AA, Adekoya AF, Weyori BA. Improving the financial security of national health insurance using cloud-based blockchain technology application. Int J Inform Manag Data Insights. 2022; 2(1):100081. doi: 10.1016/j.jjimei.2022.100081

[13] Lodha L, Baghela VS, Bhatt R. A blockchain-based secured system using the Internet of medical things (IOMT) network for e-healthcare monitoring. Meas Sens. 2023; 30:100904.

[14] Maher M, Khan I, Prikshat V. Monetisation of digital health data through a GDPR-compliant and blockchain enabled digital health data marketplace: A proposal to enhance patient’s engagement with health data repositories. Int J Inf Manag Data Insights. 2023; 3:100159. doi: 10.1016/j.jjimei.2023.100159

[15] Hennebelle A, Ismail L, Materwal H, Kaabi JA, Ranjan P, Janardhanan R. Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction. Comput Struct Biotechnol J. 2014; 23:212-233. doi: 10.1016/j.csbj.2023.11.038

[16] Mohammed MA, Lakhan A, Zebari DA, et al. Securing healthcare data in industrial cyber-physical systems using combining deep learning and blockchain technology. Eng Appl Artif Intell. 2024; 129:107612. doi: 10.1016/j.engappai.2023.107612

[17] Azbeg K, Ouchetto Q, Andaloussi SJ. BlockMedCare: A healthcare system based on IoT, Blockchain and IPFS for data management security. Egypt Inform J. 2022; 23(2):329-343. doi: 10.1016/j.eij.2022.02.004

[18] Jena SK, Kumar B, Mohanty B, Singhal A, Barik RC. An advanced blockchain-based hyperledger fabric solution for tracing fraudulent claims in the healthcare industry. Decis Anal J. 2024; 10:100411. doi: 10.1016/j.dajour.2024.100411

[19] Varela-Vaca AJ, Gasca RM, Iglesias D, Gónzalez- Gutiérrez JM. Automated trusted collaborative processes through blockchain & IoT integration: The fraud detection case. Internet Things. 2024; 25:101106.doi: 10.1016/j.iot.2024.101106

[20] Sutradhar S, Majumder S, Bose R, Mondal H, Bhattacharyya D. A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things. Decis Anal J. 2024; 10:100419. doi: 10.1016/j.dajour.2024.100419

[21] Taloba AI, Elhadad A, Park C, et al. A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare. Alex Eng J. 2022; 65:263-274.

[22] Zheng H, You K, Hu G. A novel insurance claim blockchain scheme based on zero-knowledge proof technology. Comput Commun. 2022; 195:207-216. doi: 10.1016/j.comcom.2022.08.007

[23] Antwi M, Adnane A, Kerrache CA, et al. The case of HyperLedger fabric as a blockchain solution for healthcare applications. Blockchain Res Appl. 2021; 2:100012. doi: 10.1016/j.bcra.2021.100012

[24] Xie L, Han T, Zhou H, Zhang ZR, Han B, Tang A. Tuna swarm optimization: A novel swarm-based metaheuristic algorithm for global optimization. Comput Intell Neurosci. 2021; 2021:9210050. doi: 10.1155/2021/9210050

[25] Trojovský P, Dehghani M, Hanuš P. Siberian tiger optimization: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems. IEEE Access. 2022; 10:132396-132431. doi: 10.1109/ACCESS.2022.3229964

[26] Verma G. Blockchain-based privacy preservation framework for healthcare data in cloud environment. J Exp Theor Artif Intell. 2022; 36:147-160. doi: 10.3390/electronics13193832

[27] Irshad RR, Sohail S, Hussain S, et al. A Multi-objective bee foraging learning-based particle swarm optimization algorithm for enhancing the security of healthcare data in cloud system. IEEE Access. 2023; 11:113410-113421. doi: 10.1109/ACCESS.2023.3265954

[28] Available from: https://archive.ics.uci.edu/dataset/45/heart+disease [Last accessed on 25 Jul 2024].

[29] Zhao Z, Jian Z, Gaba GS, Alroobaea R, Masud M, Rubaiee S. An improved association rule mining algorithm for large data. J Intell Syst. 2021; 30(1):750-762. doi: 10.1515/jisys-2020-0121

[30] Ahamad D, Hameed SA, Akhtar M. A multi-objective privacy preservation model for cloud security using hybrid Jaya-based shark smell optimization. J King Saud Univ Comput Inform Sci. 2022; 34(6):2343-2358. doi: 10.1016/j.jksuci.2020.10.015

[31] Baffour KA, Osei-Bonsu C, Adekoya AF. A modified apriori algorithm for fast and accurate generation of frequent item sets. Int J Sci Technol Res. 2017; 6(8):169-173.

[32] Saraswat B, Kumar A, Sharma S, Anand KB. Health chain-block chain based electronic healthcare record system with access and permission management. Meas Sens. 2023; 30:100903. doi: 10.1016/j.measen.2023.100903

[33] Alsuqaih HN, Hamdan W, Elmessiry H, Abulkasim H. An efficient privacy-preserving control mechanism based on blockchain for E-health applications. Alex Eng J. 2023; 73:159-172. doi: 10.1016/j.aej.2023.04.037

[34] Wang Y. Data structure and privacy protection analysis in big data environment based on blockchain technology. Int J Intell Netw. 2024; 5:120-132. doi: 10.1016/j.ijin.2024.02.005

[35] Kumar M, Raj H, Chaurasia N, Gill SS. Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0. Internet Things Cyber Phys Syst. 2023; 3:309-322.

[36] Sutradhar S, Karforma S, Bose R, Roy S, Djebali S, Bhattacharyya D. Enhancing identity and access management using Hyperledger Fabric and OAuth 2.0: A block-chain-based approach for security and scalability for healthcare industry. Internet Things Cyber-Phys Syst. 2024; 4:49-67. doi: 10.1016/j.iotcps.2023.07.004

[37] Hemalatha T, Bhuvaneswari A, Poornima N, et al. Secure and private data sharing in CPS e-health systems based on CB-SMO techniques. Meas Sens. 2023; 27:100787. doi: 10.1016/j.measen.2023.100787

[38] Yi H. Improving cloud storage and privacy security for digital twin based medical records. J Cloud Comp. 2023; 12:151. doi: 10.1186/s13677-023-00523-6

[39] Kumar P, Kumar R, Gupta GP, Tripathi R, Jolfaei A, Najmul Islam AKM. A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system. J Parallel Distrib Comput. 2023; 172:69-83. doi: 10.1016/j.jpdc.2022.10.002

[40] Solanas A, Patsakis C, Conti M, et al. Smart health: A context-aware health paradigm within smart cities. IEEE Commun Mag. 2014; 52(8):74-81. doi: 10.1109/MCOM.2014.6871673

[41] Prasanna KL, Rao YN. Context-Aware Approaches in Iot-Based Healthcare Systems Using Deep Learning Techniques: A Study. In: 2024 3^rdInternational Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India; 2024. p. 567-570. doi: 10.1109/ICAAIC60222.2024.10575875

[42] Aazad SK, Saini T, Ajad A, Chaudhary K, Elsayed K. Deciphering blood cells - method for blood cell analysis using microscopic images. J Modern Technol. 2024; 1(1):9-18.

[43] Kalnoor G, Dasari KS, Suma S, Waddenkery N, Pragathi B. Enhanced brain tumor detection from MRI scans using frequency domain features and hybrid machine learning models. J Mod Technol. 2025; 1(2):141-149. doi: 10.1007/s00521-025-11031-w