A Unified Framework for Secure Healthcare Data Sharing: Integrating Federated Learning, Blockchain, and Quantum Cryptography

Jeremie Ruvunangiza*; Carlos Valderrama

**Jeremie Ruvunangiza* and Carlos Valderrama**

Volume5-Issue9
Dates: Received: 2024-08-30 | Accepted: 2024-09-05 | Published: 2024-09-11
Pages: 1081-1088

Abstract

As the demand for secure and efficient data sharing in healthcare continues to grow, there is a pressing need for innovative solutions that ensure data privacy, integrity, and accessibility in multiple institutions. This study proposes a unified framework that integrates three cutting-edge technologies, federated learning, blockchain, and quantum cryptography, to address the complex challenges of secure data sharing in the healthcare sector. Federated learning enables decentralized data analysis by maintaining sensitive patient information locally, significantly reducing the risk of data breaches. Blockchain technology adds an immutable and transparent ledger to securely track data exchanges, ensuring compliance with stringent data governance standards. Quantum cryptography enhances the security of data transmission using quantum mechanics principles to prevent unauthorized access and guarantee the confidentiality of shared information. The proposed framework successfully combines these advanced technologies to fortify the security of healthcare data sharing. Promote collaborative analysis while maintaining patient privacy, leading to better patient outcomes and fostering greater trust among healthcare providers. By synergizing federated learning, blockchain, and quantum cryptography, the proposed framework represents a significant advance in secure healthcare data sharing. Not only does it address the urgent need for data security, it also supports global collaboration necessary to tackle healthcare challenges on an international scale.

FullText HTML FullText PDF DOI: 10.37871/jbres1993

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How to cite this article

Ruvunangiza J, Valderrama C. A Unifi ed Framework for Secure Healthcare Data Sharing: Integrating Federated Learning, Blockchain, and Quantum Cryptography. J Biomed Res Environ Sci. 2024 Sept 11; 5(9): 1081-1088. doi: 10.37871/jbres1993, Article ID: JBRES1993, Available at: https://www.jelsciences.com/articles/jbres1993.pdf

Subject area(s)

Public Health

References

Mäntymäki M, Tandon A, Dhir A, Najmul Islam AKM. Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda. Elsevier BV. 2020;122:103290. doi: 10.1016/j.compind.2020.103290.
Schwalbe N, Wahl B, Song J, Lehtimaki S. Data Sharing and Global Public Health: Defining What We Mean by Data. Front Digit Health. 2020 Dec 14;2:612339. doi: 10.3389/fdgth.2020.612339. PMID: 34713073; PMCID: PMC8521885.
Taghizade S, Chattu VK, Jaafaripooyan E, Kevany S. COVID-19 Pandemic as an Excellent Opportunity for Global Health Diplomacy. Front Public Health. 2021 Jul 12;9:655021. doi: 10.3389/fpubh.2021.655021. PMID: 34322467; PMCID: PMC8310918.
Scheibner J, Raisaro JL, Troncoso-Pastoriza JR, Ienca M, Fellay J, Vayena E, Hubaux JP. Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis. J Med Internet Res. 2021 Feb 25;23(2):e25120. doi: 10.2196/25120. PMID: 33629963; PMCID: PMC7952236.
Zhang R, Xue R, Liu L. Security and privacy for healthcare blockchains. Cornell University. 2021. doi: 10.48550/arXiv.2106.06136.
Abdulrahman H, Poh N, Burnett J. Privacy preservation, sharing and collection of patient records using cryptographic techniques for cross-clinical secondary analytics. 2014. doi: 10.1109/cicare.2014.7007847.
Kim D, Kim H, Kwak J. Secure sharing scheme of sensitive data in the precision medicine system. 2020;64:1527-53. doi: 10.32604/cmc.2020.010535.
Pisani E, Aaby P, Breugelmans JG, Carr D, Groves T, Helinski M, Kamuya D, Kern S, Littler K, Marsh V, Mboup S, Merson L, Sankoh O, Serafini M, Schneider M, Schoenenberger V, Guerin PJ. Beyond open data: realising the health benefits of sharing data. BMJ. 2016 Oct 10;355:i5295. doi: 10.1136/bmj.i5295. PMID: 27758792; PMCID: PMC6616027.
Khan RA, Shah SM. Secondary use of electronic health record: Opportunities and challenges. Institute of Electrical and Electronics Engineers. 2020;8:136947-65. doi: 10.1109/access.2020.3011099.
Dankar FK. Practices and challenges in clinical data sharing. Cornell University. 2023. doi: 10.48550/arXiv.2304.06509.
Porwal S, Srijith K. Nair SK, Dimitrakos T. Regulatory impact of data protection and privacy in the cloud. Springer Science+Business Media. 2011;290-299. doi: 10.1007/978-3-642-22200-9_23.
Bhavin M, Tanwar S, Sharma N, Tyagi S, Kumar N. Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications. 2023. doi: 10.1016/j.jisa.2020.102673.
Selvarajan S, Mouratidis H. A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems. Sci Rep. 2023 May 2;13(1):7107. doi: 10.1038/s41598-023-34354-x. Erratum in: Sci Rep. 2023 Jun 9;13(1):9409. doi: 10.1038/s41598-023-36573-8. PMID: 37131047; PMCID: PMC10154383.
Bhatia AS, Neira DEB. Federated hierarchical tensor networks: A collaborative learning quantum AI-driven framework for healthcare. Cornell University. 2024. doi: 10.48550/arXiv.2405.07735.
Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. Springer Science+Business Media. 2020;5:1-19. doi: 10.1007/s41666-020-00082-4.
Nguyen LT, Nguyen LD, Hoang T, Bandara D, Wang Q, Lu Q, Xu X, Zhu L, Popovski P, Chen S. Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges. 2023. doi: 10.48550/arXiv.2303.06546.
Chen L, Yang J-J, Wang Q, Niu Y. A framework for privacy-preserving healthcare data sharing. 2012 IEEE 14th international conference on e-health networking, applications and services (healthcom). IEEE. 2012;341-6. doi: 10.1109/HealthCom.2012.6379433.
Science & tech spotlight: Securing data for a post-quantum world. 2023.
Barker W, Polk W, Souppaya M. Getting ready for post-quantum cryptography: Exploring challenges associated with adopting and using post-quantum cryptographic algorithms 2021. doi: 10.6028/nist.cswp.15.
Vithanwattana N, Karthick G, Mapp G, George C, Samuels A. Securing future healthcare environments in a post-COVID-19 world: moving from frameworks to prototypes. J Reliab Intell Environ. 2022;8(3):299-315. doi: 10.1007/s40860-022-00180-7. Epub 2022 Jul 9. PMID: 35967078; PMCID: PMC9362615.
Chehimi M, Saad W. Quantum federated learning with quantum data. ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. 2022;8617-21. doi: 10.1109/ICASSP43922.2022.9746622.
Xia Q, Li Q. Quantumfed: A federated learning framework for collaborative quantum training. 2021 IEEE global communications conference (GLOBECOM). IEEE. 2021;1-6. doi: 10.1109/GLOBECOM46510.2021.9685012.
Sarmadi A, Fu H, Krishnamurthy P, Garg S, Khorrami F. Privacy-preserving collaborative learning through feature extraction. Cornell University. 2022. doi: 10.48550/arXiv.2212.06322
Zou R, Lv X, Zhao J. SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system. Elsevier BV. 2021;58:102604-4. doi: 10.1016/j.ipm.2021.102604.
Gurung D, Pokhrel SR, Li G. Decentralized quantum federated learning for metaverse: Analysis, design and implementation. Cornell University. 2023. doi: 10.48550/arXiv.2306.11297.
Wang J, Huberman B. A guide to global quantum key distribution networks. Cornell University. 2020. doi: 10.48550/arXiv.2012.14396.
Zhang P, Schmidt DC, White J. A pattern sequence for designing blockchain-based healthcare information technology systems. Cornell University. 2020. doi: 10.48550/arXiv.2010.01172.
Liu D, Miller T, Sayeed R, Mandl KD. FADL: Federated-autonomous deep learning for distributed electronic health record. arXiv preprint arXiv:181111400. 2018. doi: 10.48550/arXiv.1811.11400.
Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. Journal of healthcare informatics research. 2021;5:1-19.
Jain N, Hoff U, Gambetta M, Rodenberg J, Gehring T. Quantum key distribution for data center security–a feasibility study. arXiv preprint. 2023. doi: 10.48550/arXiv.2307.13098
Goh E, Kim D-Y, Lee K, Oh S, Chae J-E, Kim D-Y. Blockchain-enabled federated learning: A reference architecture design, implementation, and verification. IEEE Access. 2023. doi: 10.48550/arXiv.2306.10841.
Dara T, Joong-Sun L, Hiroyuki S, Anushka W, Naoko T, Takashi O, Nagaaki O. Application of blockchain to maintaining patient records in electronic health record for enhanced privacy, scalability, and availability.2020;26:3-3. doi: 10.4258/hir.2020.26.1.3.
Zhang Y, Zhang C, Zhang C, Fan L, Zeng B, Yang Q. Federated learning with quantum secure aggregation. Cornell University. 2022. doi: 10.48550/arXiv.2207.07444.
Dubovitskaya A, Xu Z, Ryu S, Schumacher M, Wang F. Secure and Trustable Electronic Medical Records Sharing using Blockchain. AMIA Annu Symp Proc. 2018 Apr 16;2017:650-659. PMID: 29854130; PMCID: PMC5977675.
Yoo JH, Jeong H, Lee J, Tai-Myoung C. Federated learning: Issues in medical application. Cornell University. 2021. doi: 10.48550/arXiv.2109.00202.
Bhavin M, Tanwar S, Sharma N, Tyag S, Neeraj k. Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications. 2020. doi: 10.1016/j.jisa.2020.102673.
Pandey PSAJK. Secure quantum computing for healthcare sector: A short analysis. Cornell University. 2022. doi: 10.48550/arXiv.2211.10027.
Simon PDAF, Simon F. Differences between Europe and the united states on AI/digital policy: Comment response to roundtable discussion on AI. SAGE Publishing. 2020;4:247028972090710-0. doi: 10.1177/2470289720907103.
Dev Gurung, Shiva Raj Pokhrel, Gang Li. Performance analysis and evaluation of post quantum secure blockchained federated learning. Cornell University. 2023. doi: 10.48550/arXiv.2306.14772.
Nitin Jain, Ulrich Hoff, Marco Gambetta, Jesper Rodenberg, Tobias Gehring. Quantum key distribution for data center security- A feasibility study Cornell University. 2023. doi: 10.48550/arXiv.2307.13098.