PPVFL-SplitNN: Privacy-Preserving Vertical Federated Learning with Split Neural Networks for Distributed Patient Data

Bashair Alrashed, Priyadarsi Nanda, Hoang Dinh, Amani Aldahiri, Hadeel Alhosaini, Nojood Alghamdi

2025

Abstract

Medical data privacy regulations pose significant challenges for sharing raw data between healthcare institutions. These challenges are particularly critical when the data is vertically partitioned. In such scenarios, each healthcare provider holds unique but complementary patient information. This makes collaborative learning challenging while protecting patient privacy. As a result, developing effective machine learning models that require integrated data becomes unfeasible. This leads to fragmented analyses and less effective patient care. To address this issue, we developed a vertical federated learning framework using split neural networks to enable secure collaboration while preserving privacy. The framework comprises three main stages: generating symmetric keys to establish secure communication, aligning overlapping patient records across institutions using a privacy-preserving record linkage algorithm, and collaboratively training a global machine learning model without revealing patient privacy. We evaluated the framework on three well-known medical datasets. Our evaluation focused on two critical scenarios: varying degrees of overlap in patient records and differing feature distributions. The proposed framework ensures patient privacy and compliance with strict regulations, providing a scalable and practical solution for real-world healthcare networks. It effectively addresses key challenges in privacy-preserving collaborative machine learning.

Paper Citation

in Harvard Style

Alrashed B., Nanda P., Dinh H., Aldahiri A., Alhosaini H. and Alghamdi N. (2025). PPVFL-SplitNN: Privacy-Preserving Vertical Federated Learning with Split Neural Networks for Distributed Patient Data. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 13-24. DOI: 10.5220/0013445300003979

in Bibtex Style

@conference{secrypt25,
author={Bashair Alrashed and Priyadarsi Nanda and Hoang Dinh and Amani Aldahiri and Hadeel Alhosaini and Nojood Alghamdi},
title={PPVFL-SplitNN: Privacy-Preserving Vertical Federated Learning with Split Neural Networks for Distributed Patient Data},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={13-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013445300003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - PPVFL-SplitNN: Privacy-Preserving Vertical Federated Learning with Split Neural Networks for Distributed Patient Data
SN - 978-989-758-760-3
AU - Alrashed B.
AU - Nanda P.
AU - Dinh H.
AU - Aldahiri A.
AU - Alhosaini H.
AU - Alghamdi N.
PY - 2025
SP - 13
EP - 24
DO - 10.5220/0013445300003979
PB - SciTePress