Ensuring Data Security and Privacy in Generative AI-Based Healthcare Systems: Challenges and Solutions

Authors

  • Viswanatha raju Sangaraju

Keywords:

Generative AI, Healthcare Data Privacy, Data Security, Differential Privacy, Federated Learning.

Abstract

Generative AI Transforming Healthcare through Improved Diagnostics and PersonalizedTreatment But deploying these models in sensitive healthcare environments raises serious datasecurity and patient privacy issues. This work addresses the critical security of healthcare data thatunderpins generation systems, including issues such as data leakage, model inversion attacks andunauthorized access to synthetic outputs. It analyzes upturns and ethical implications under guides

References

Goodfellow, I., et al. (2014). Generative Adversarial Networks (GANs). Proceedings of the Neural Information Processing Systems (NeurIPS), 2672–2680. https://doi.org/10.5555/2999792.2999956

Choi, E., et al. (2019). Generative models in healthcare. Journal of Healthcare AI, 12(3), 45-58. https://doi.org/10.1007/jhci.2019.0045

Downloads

Published

2024-12-24

How to Cite

Viswanatha raju Sangaraju. (2024). Ensuring Data Security and Privacy in Generative AI-Based Healthcare Systems: Challenges and Solutions. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1204–1222. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2545

Issue

Section

Articles