2023
Privacy-Preserving Federated Learning for Healthcare Data
4 citations
Privacy Preservation and Secured Data Storage in Cloud Computing, pp. 178-196 (book chapter)
Differentially private model release for healthcare applications
International Journal of Computers and Applications 44(10), 953-958 · 2022 · cited 16 times
The question behind it
Can a hospital share what it learned without sharing its patients?
Health records are the most sensitive data there is, and anonymised releases have been re-identified before. This line of work releases the trained model instead of the data, and looks at federated learning, where the data never leaves the hospital.
A plain-language summary of this paper is on its way here. The full text will follow wherever the publisher permits me to host it.
Cite this paper
@article{sangeetha2022differentially,
title = {Differentially private model release for healthcare applications},
author = {S Sangeetha and G Sudha Sadasivam and A Srikanth},
journal = {International Journal of Computers and Applications 44(10), 953-958},
year = {2022},
doi = {10.1080/1206212X.2021.2024958},
url = {https://www.tandfonline.com/doi/abs/10.1080/1206212X.2021.2024958},
}