A Framework for Federated Analysis of Health Data Using Multiparty Homomorphic Encryption

Miroslav Puskaric

2024

Abstract

Although federated data analysis represents a significant contribution toward ensuring data privacy, the risk of information leakage from the intermediate results exchanged during the analysis process still exists. These risks become even more emphasised when analysing sensitive data such as health records. One of the approaches to mitigate these issues is homomorphic encryption, a novel encryption algorithm which allows for performing computations over encrypted data. This article presents a federated data analysis framework where intermediate analysis results are exchanged and processed as ciphertexts and where data sources are connected in a decentralised manner by forming multiple clusters, with each cluster having a central node. Besides processing encrypted information, another advantage of the homomorphic encryption algorithms is the support for a multiparty encryption scheme. A workflow for creating a shared public and evaluation key is presented, where central nodes are part of the workflow and data sources only receive the shared keys. Furthermore, as data analysis examples, workflows for Kaplan-Meier survival analysis and distributed mean value are presented, whose results do match those obtained through centralized analysis. As a last step of the federated data analysis, multiparty decryption of the final result occurs.

Paper Citation

in Harvard Style

Puskaric M. (2024). A Framework for Federated Analysis of Health Data Using Multiparty Homomorphic Encryption. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 661-667. DOI: 10.5220/0012753900003767

in Bibtex Style

@conference{secrypt24,
author={Miroslav Puskaric},
title={A Framework for Federated Analysis of Health Data Using Multiparty Homomorphic Encryption},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={661-667},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012753900003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - A Framework for Federated Analysis of Health Data Using Multiparty Homomorphic Encryption
SN - 978-989-758-709-2
AU - Puskaric M.
PY - 2024
SP - 661
EP - 667
DO - 10.5220/0012753900003767
PB - SciTePress