Proceedings of International Conference on Applied Innovation in IT
2025/07/26, Volume 13, Issue 3, pp.141-148

Blockchain-Driven Security, Privacy and Reliability for Digital Healthcare Systems


Radhiya Sulaiman Nasser Alhabsi and Alla Salim Mohammed Almukhaini


Abstract: With the integration of digital technologies in healthcare, several transformative advances have been made, including the management and sharing of patient data. Security, privacy, and system reliability are among the challenges presented by digitizing health data. Health data integrity, confidentiality, and reliability are all ensured by blockchain technology because of its decentralized, immutable nature. The purpose of this paper is to explore the potential of blockchain technology by using it to address medical imaging, diagnosis, and secure data sharing. Combining distributed ledger technology and cryptography can improve the security, privacy, and interoperability of healthcare systems. Using blockchain-driven AI models, the study illustrates how medical imaging applications can be significantly enhanced through secure, auditable, and transparent data-sharing practices, thus increasing stakeholder trust and substantially enhancing the scalability and reliability of AI-driven systems. By effectively combining blockchain's decentralized security features with advanced artificial intelligence techniques, diagnostic accuracy can be improved, patient care and clinical decision-making processes can be optimized, and strict regulatory compliance in medical imaging environments can be consistently maintained. This integrated approach also facilitates smoother collaboration among medical professionals.

Keywords: Iot-Blockchain Platform, Data Integrity, Smart Contracts, Sensor Data Management, Decentralized Security.

DOI: Under Indexing

Download: PDF

References:

  1. P. Rani, S. Verma, S. P. Yadav, B. K. Rai, M. S. Naruka, and D. Kumar, "Simulation of the lightweight blockchain technique based on privacy and security for healthcare data for the cloud system," Int. J. E-Health Med. Commun. (IJEHMC), vol. 13, no. 4, pp. 1–15, 2022.
  2. A. Singh et al., "Blockchain-Based Lightweight Authentication Protocol for Next-Generation Trustworthy Internet of Vehicles Communication," IEEE Trans. Consum. Electron., vol. 70, no. 2, pp. 4898–4907, May 2024. [Online]. Available: https://doi.org/10.1109/TCE.2024.3351221.
  3. K. Devarapu, K. Rahman, A. Kamisetty, and D. Narsina, "MLOps-Driven Solutions for Real-Time Monitoring of Obesity and Its Impact on Heart Disease Risk: Enhancing Predictive Accuracy in Healthcare," Int. J. Reciprocal Symmetry Theor. Phys., vol. 6, pp. 43–55, 2019.
  4. C. R. Thompson, R. R. Talla, J. C. S. Gummadi, and A. Kamisetty, "Reinforcement Learning Techniques for Autonomous Robotics," Asian J. Appl. Sci. Eng., vol. 8, no. 1, pp. 85–96, 2019.
  5. P. K. Gade, "MLOps Pipelines for GenAI in Renewable Energy: Enhancing Environmental Efficiency and Innovation," Asia Pac. J. Energy Environ., vol. 6, no. 2, pp. 113–122, Dec. 2019. [Online]. Available: https://doi.org/10.18034/apjee.v6i2.776.
  6. R. K. Karanam et al., "Neural Networks in Algorithmic Trading for Financial Markets," Asian Account. Audit. Adv., vol. 9, no. 1, pp. 115–126, 2018.
  7. R. Mohammed et al., "Optimizing Web Performance: Front End Development Strategies for the Aviation Sector," Int. J. Reciprocal Symmetry Theor. Phys., vol. 4, pp. 38–45, 2017.
  8. D. Narsina et al., "AI-Driven Database Systems in FinTech: Enhancing Fraud Detection and Transaction Efficiency," Asian Account. Audit. Adv., vol. 10, no. 1, pp. 81–92, 2019.
  9. M. Rodriguez et al., "Oracle EBS and Digital Transformation: Aligning Technology with Business Goals," Technol. Manag. Rev., vol. 4, pp. 49–63, 2019.
  10. P. Rani, K. Ur Rehman, S. P. Yadav, and L. Hussein, "Deep Learning and AI in Behavioral Analysis for Revolutionizing Mental Healthcare," in Demystifying the Role of Natural Language Processing (NLP) in Mental Health, A. Mishra et al., Eds., IGI Global, 2025, pp. 263–282. [Online]. Available: https://doi.org/10.4018/979-8-3693-4203-9.ch014.
  11. H. P. Kommineni, "Cognitive Edge Computing: Machine Learning Strategies for IoT Data Management," Asian J. Appl. Sci. Eng., vol. 8, no. 1, pp. 97–108, Oct. 2019. [Online]. Available: https://doi.org/10.18034/ajase.v8i1.123.
  12. S. Kothapalli et al., "Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability," ABC Res. Alert, vol. 7, no. 3, pp. 193–204, Dec. 2019. [Online]. Available: https://doi.org/10.18034/ra.v7i3.663.
  13. R. R. Kundavaram et al., "Predictive Analytics and Generative AI for Optimizing Cervical and Breast Cancer Outcomes: A Data-Centric Approach," ABC Res. Alert, vol. 6, no. 3, pp. 214–223, Dec. 2018. [Online]. Available: https://doi.org/10.18034/ra.v6i3.672.
  14. P. Rani, D. S. Mohan, S. P. Yadav, G. K. Rajput, and M. A. Farouni, "Sentiment Analysis and Emotional Recognition: Enhancing Therapeutic Interventions," in Demystifying the Role of Natural Language Processing (NLP) in Mental Health, A. Mishra et al., Eds., IGI Global, 2025, pp. 283–302. [Online]. Available: https://doi.org/10.4018/979-8-3693-4203-9.ch015.
  15. E. Mezghani, E. Exposito, and K. Drira, "A Model-Driven Methodology for the Design of Autonomic and Cognitive IoT-Based Systems: Application to Healthcare," IEEE Trans. Emerg. Top. Comput. Intell., vol. 1, no. 3, pp. 224–234, Jun. 2017. [Online]. Available: https://doi.org/10.1109/TETCI.2017.2699218.
  16. M. N. Kadhim, A. H. Mutlag, D. A. Hammood, and N. B. H. Ismail, "Identification of Vehicle Logos in Deep Learning: A Comprehensive Survey," JT, vol. 7, no. 1, pp. 37–47, Mar. 2025.
  17. G. Tomasicchio, A. Ceccarelli, A. D. Matteis, and L. Spazzacampagna, "A space-based healthcare emergency management system for epidemics monitoring and response," in IET Conf. Proc., vol. 2021, no. 14, pp. 195–199, Apr. 2022. [Online]. Available: https://doi.org/10.1049/icp.2022.0571.
  18. S. Verma et al., "An automated face mask detection system using transfer learning based neural network to preventing viral infection," Expert Syst., p. e13507, 2024.
  19. A. F. Subahi, "Edge-Based IoT Medical Record System: Requirements, Recommendations and Conceptual Design," IEEE Access, vol. 7, pp. 94150–94159, 2019. [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2927958
  20. M. U. Rehman et al., "A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis," IEEE Trans. Netw. Sci. Eng., vol. 9, no. 6, pp. 4322–4337, Nov. 2022. [Online]. Available: https://doi.org/10.1109/TNSE.2022.3199235.
  21. D. Miranda, R. Olivares, R. Munoz, and J.-G. Minonzio, "Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 69, no. 9, pp. 2663–2671, Sep. 2022. [Online]. Available: https://doi.org/10.1109/TUFFC.2022.3195477.
  22. M. Wazid, J. Singh, A. K. Das, S. Shetty, M. K. Khan, and J. J. P. C. Rodrigues, "ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things," IEEE Access, vol. 10, pp. 57990–58004, 2022. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3179418.
  23. G. Ansari, P. Rani, and V. Kumar, "A novel technique of mixed gas identification based on the group method of data handling (GMDH) on time-dependent MOX gas sensor data," in Proc. Int. Conf. Recent Trends Comput. (ICRTC 2022), Springer, 2023, pp. 641–654.
  24. C. M. Parra, M. Gupta, and D. Dennehy, "Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias," IEEE Trans. Technol. Soc., vol. 3, no. 1, pp. 41–45, Mar. 2022. [Online]. Available: https://doi.org/10.1109/TTS.2021.3120303.
  25. H. Elayan, M. Aloqaily, and M. Guizani, "Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated Learning," IEEE Internet Things J., vol. 9, no. 10, pp. 7338–7346, May 2022. [Online]. Available: https://doi.org/10.1109/JIOT.2021.3103635.
  26. P. Rani, S. P. Yadav, P. N. Singh, and M. Almusawi, "Real-World Case Studies: Transforming Mental Healthcare With Natural Language Processing," in Demystifying the Role of Natural Language Processing (NLP) in Mental Health, A. Mishra et al., Eds., IGI Global, 2025, pp. 303–324. [Online]. Available: https://doi.org/10.4018/979-8-3693-4203-9.ch016.
  27. B. C. Tedeschini et al., "Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation," IEEE Access, vol. 10, pp. 8693–8708, 2022. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3141913.
  28. N. A. Kadhim, A. A. Obed, A. J. Abid, A. L. Saleh, and R. J. Hassoon, "A Systematic Review for Reconfiguring Photovoltaic Arrays under Conditions of Partial Shading," EETJ, vol. 1, no. 1, pp. 20–34, Jun. 2024.
  29. G. Subramanian and A. Sreekantan Thampy, "Implementation of Blockchain Consortium to Prioritize Diabetes Patients’ Healthcare in Pandemic Situations," IEEE Access, vol. 9, pp. 162459–162475, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3132302.
  30. P. Rani, U. C. Garjola, and H. Abbas, "A Predictive IoT and Cloud Framework for Smart Healthcare Monitoring Using Integrated Deep Learning Model," NJF Intell. Eng. J., vol. 1, no. 1, pp. 53–65, 2024.
  31. V. K. Prasad, M. D. Bhavsar, and S. Tanwar, "Influence of Montoring: Fog and Edge Computing," Scalable Comput. Pract. Exp., vol. 20, no. 2, pp. 365–376, May 2019. [Online]. Available: https://doi.org/10.12694/scpe.v20i2.1533.


    HOME

       - Conference
       - Journal
       - Paper Submission to Journal
       - Paper Submission to Conference
       - For Authors
       - For Reviewers
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 13, Issue 3 (ICAIIT 2025)
       - Volume 13, Issue 2 (ICAIIT 2025)
       - Volume 13, Issue 1 (ICAIIT 2025)
       - Volume 12, Issue 2 (ICAIIT 2024)
       - Volume 12, Issue 1 (ICAIIT 2024)
       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2025
         - Photos
         - Reports

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.