Database security in psychiatry: leveraging large language models and blockchain for secure data managemente Data Management

Authors

  • Joel Nithish Kumar Murugan * Illinois Institute of Technology, Chicago, Illinois, United States of America
  • Padmavathi Vyshnavi Madarampalli Illinois Institute of Technology, Chicago, Illinois, United States of America
  • Shashikant Ramesh Yadav Illinois Institute of Technology, Chicago, Illinois, United States of America

https://doi.org/10.22105/metaverse.v2i1.44

Abstract

In this digital era, secure management of psychiatric data has become a key challenge due to the highly sensitive nature of the information it encompasses- from personal histories to therapeutic notes and behaviors up to diagnostic findings. Its unauthorized access or inappropriate handling might lead to privacy breaches, discrimination, and further erosion of trust in these mental health systems. This paper discusses how two disruptive technologies, Large Language Models (LLM) and blockchain, come together to address these challenges. The LLM is particularly good at analyzing unstructured data and, as such, provides deep, insightful clinical analytics for personalized mental health interventions while maintaining patient confidentiality. Blockchain technology ensures data integrity, immutability, and decentralized storage for the robustness of security and controlled access to sensitive psychiatric records. The combination of technologies will yield a secure, efficient, and privacy-preserving system for managing psychiatric data, advanced clinical decision-making, and trust protection of patients. The proposed framework will include a blockchain-based decentralized storage layer, a data analysis layer powered by LLMs, and a secure interface for controlled access to data with ethical and regulatory compliance. This integration is a significant step forward in attending to the special needs that psychiatric data management presents within healthcare.

Keywords:

Large language models, Blockchain, Natural language processing , Data access control, Secure data storage

References

  1. [1] Balajee, R. M., Mohapatra, H., & Venkatesh, K. (2021). A comparative study on efficient cloud security, services, simulators, load balancing, resource scheduling and storage mechanisms. IOP conference series: materials science and engineering (Vol. 1070, p. 012053). IOP Publishing. https://doi.org/10.1088/1757-899x/1070/1/012053
  2. [2] Swain, B., Raj, P., Singh, K., Singh, Y., Singh, S., & Mohapatra, H. (2025). Ethical implications and mitigation strategies for public safety and security in smart cities for securing tomorrow. In Convergence of cybersecurity and cloud computing (pp. 419–436). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6859-6.ch019
  3. [3] Act, A. (1996). Health insurance portability and accountability act of 1996. Public law, 104, 191. https://aspe.hhs.gov/reports/health-insurance-portability-accountability-act-1996
  4. [4] Zhou, B., Yang, G., Shi, Z., & Ma, S. (2022). Natural language processing for smart healthcare. IEEE reviews in biomedical engineering, 17, 4–18. https://doi.org/10.1109/RBME.2022.3210270
  5. [5] Hasselgren, A., Kralevska, K., Gligoroski, D., Pedersen, S. A., & Faxvaag, A. (2020). Blockchain in healthcare and health sciences—A scoping review. International journal of medical informatics, 134, 104040. https://doi.org/10.1016/j.ijmedinf.2019.104040
  6. [6] Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2023). Artificial intelligence and blockchain integration in business: trends from a bibliometric-content analysis. Information systems frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022-10279-0
  7. [7] Heston, T. F. (2024). Prespective chapter: integrating large language models and blockchain in telemedicine. IntechOpen. https://www.intechopen.com/chapters/1176440
  8. [8] Omar, M., Soffer, S., Charney, A. W., Landi, I., Nadkarni, G. N., & Klang, E. (2024). Applications of large language models in psychiatry: A systematic review. Frontiers in psychiatry, 15, 2003–2024. https://doi.org/10.3389/fpsyt.2024.1422807
  9. [9] Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019). Blockchain technology in healthcare: A systematic review. Healthcare, 7(2), 56. https://doi.org/10.3390/healthcare7020056
  10. [10] Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ digital medicine, 3(1), 1–7. https://doi.org/10.1038/s41746-020-00323-1
  11. [11] Terra, M., Baklola, M., Ali, S., & El-Bastawisy, K. (2023). Opportunities, applications, challenges and ethical implications of artificial intelligence in psychiatry: A narrative review. The egyptian journal of neurology, psychiatry and neurosurgery, 59(1), 80. https://doi.org/10.1186/s41983-023-00681-z
  12. [12] Marková, I. S., & Berrios, G. E. (1992). The assessment of insight in clinical psychiatry: a new scale. Acta psychiatrica scandinavica, 86(2), 159–164. https://doi.org/10.1111/j.1600-0447.1992.tb03245.x
  13. [13] Luxton, D. D., June, J. D., & Fairall, J. M. (2012). Social media and suicide: A public health perspective. American journal of public health, 102(S2), S195-S200. https://doi.org/10.2105/AJPH.2011.300608
  14. [14] Radanović, I., & Likić, R. (2018). Opportunities for use of blockchain technology in medicine. Applied health economics and health policy, 16(5), 583–590. https://doi.org/10.1007/s40258-018-0412-8
  15. [15] Awotunde, J. B., Jimoh, R. G., Folorunso, S. O., Adeniyi, E. A., Abiodun, K. M., & Banjo, O. O. (2021). Privacy and security concerns in IoT-based healthcare systems. In The fusion of internet of things, artificial intelligence, and cloud computing in health care (pp. 105–134). Springer. https://doi.org/10.1007/978-3-030-75220-0_6
  16. [16] Kim, S. K., & Huh, J. H. (2020). Autochain platform: expert automatic algorithm blockchain technology for house rental dApp image application model. EURASIP journal on image and video processing, 2020(1), 47. https://doi.org/10.1186/s13640-020-00537-z
  17. [17] Nicholas, J., Onie, S., & Larsen, M. E. (2020). Ethics and privacy in social media research for mental health. Current psychiatry reports, 22(12), 1–7. https://doi.org/10.1007/s11920-020-01205-9
  18. [18] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: can language models be too big? [presentation]. FAccT 2021 - proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). https://doi.org/ 10.1145/3442188.3445922
  19. [19] McCradden, M., Hui, K., & Buchman, D. Z. (2023). Evidence, ethics and the promise of artificial intelligence in psychiatry. Journal of medical ethics, 49(8), 573–579. https://doi.org/10.1136/jme-2022-108447
  20. [20] Zyskind, G., Nathan, O., & others. (2015). Decentralizing privacy: using blockchain to protect personal data. 2015 IEEE security and privacy workshops (pp. 180–184). IEEE. https://doi.org/ 10.1109/SPW.2015.27
  21. [21] Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J., & Ni, W. (2020). PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities. Computers and security, 88, 101653. https://doi.org/10.1016/j.cose.2019.101653
  22. [22] Yaqoob, I., Salah, K., Uddin, M., Jayaraman, R., Omar, M., & Imran, M. (2020). Blockchain for digital twins: recent advances and future research challenges. IEEE network, 34(5), 290–298. https://doi.org/10.1109/MNET.001.1900661
  23. [23] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: concept and applications. ACM transactions on intelligent systems and technology, 10(2), 1–19. https://doi.org/10.1145/3298981

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Published

2025-03-02

Issue

Vol. 2 No. 1 (2025): Metaversalize

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Articles

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How to Cite

Kumar Murugan, J. N., Vyshnavi Madarampalli, P., & Yadav, S. R. (2025). Database security in psychiatry: leveraging large language models and blockchain for secure data managemente Data Management. Metaversalize, 2(1), 1-10. https://doi.org/10.22105/metaverse.v2i1.44
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