Faculty, Staff and Student Publications
Language
English
Publication Date
6-1-2023
Journal
Proceedings of the IEEE International Conference on Healthcare Informatics
DOI
10.1109/ichi57859.2023.00022
PMID
38152589
PMCID
PMC10751041
PubMedCentral® Posted Date
12-11-2024
PubMedCentral® Full Text Version
Author MSS
Abstract
Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also enables equitable access to computational intelligence. However, recent studies have shown that an adversary may leverage a shared model to learn the participation of a target individual in the training set. In this work, we investigate privacy-protecting model sharing for survival studies. Specifically, we pose three research questions. (1) Do deep survival models leak membership information? (2) How effective is differential privacy in defending against membership inference in deep survival analyses? (3) Are there other effects of differential privacy on deep survival analyses? Our study assesses the membership leakage in emerging deep survival models and develops differentially private training procedures to provide rigorous privacy protection. The experimental results show that deep survival models leak membership information and our approach effectively reduces membership inference risks. The results also show that differential privacy introduces a limited performance loss, and may improve the model robustness in the presence of noisy data, compared to non-private models.
Keywords
Deep Learning, Survival Analysis, Membership Inference, Data Privacy
Published Open-Access
yes
Recommended Citation
, "Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy" (2023). Faculty, Staff and Student Publications. 887.
https://digitalcommons.library.tmc.edu/uthshis_docs/887