Abstract

The sensitive nature of health data makes it difficult to develop machine learning models through inter-institutional collaboration. This review examines federated learning, differential privacy, homomorphic encryption and secure multi-party computation in the context of healthcare. An analysis of 96 studies shows significant gaps regarding the privacy-utility trade-off, data heterogeneity and regulatory compliance. Recommendations for practitioners are provided within the frameworks of KVKK and GDPR.

Keywords:privacyfederated learningdifferential privacyhealth informaticsreview

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Declarations

Ethics Approval
This study does not require ethics committee approval.
Conflict of Interest
The authors declare no conflict of interest.

References 5

  1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273–1282).
  2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  3. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
  5. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010

How to Cite

Nurlanovna, A., Mammadova, L., & Durmaz, M. A. (2026). Privacy-Preserving Machine Learning for Health Data: A Review of Federated Learning and Differential Privacy. International Journal of Science and Technology Research, 8(1), 32–50. https://doi.org/10.99999/ubtad.2026.15
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CC BY 4.0

© 2026 Aigerim Nurlanovna, Leyla Mammadova, Mehmet Ali Durmaz. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text