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Securing Federated GANs: Enabling Synthetic Data Generation for Health Registry Consortiums

Authors:

Narasimha Raghavan Veeraragavan,

Jan Franz NygårdAuthors Info & Claims

ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security

Article No.: 89, Pages 1 - 9

https://doi.org/10.1145/3600160.3605041

Published: 29 August 2023 Publication History

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Abstract

In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic data sets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Federated GANs, Consortium blockchains, and Shamir Secret Sharing algorithm are the core building blocks of our proposed architecture solution. Finally, we discuss our proposed solution’s advantages, disadvantages and future research directions.

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Cited By

Iantorno MGarza J(2024)Leveraging Artificial Intelligence Models Using Synthetic DataProceedings of the Future Technologies Conference (FTC) 2024, Volume 110.1007/978-3-031-73110-5_4(56-66)Online publication date: 5-Nov-2024
https://doi.org/10.1007/978-3-031-73110-5_4
Karimireddy SVeeraragavan NElvatun SNygård J(2023)Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC59375.2023.10305961(224-231)Online publication date: 18-Sep-2023
https://doi.org/10.1109/FMEC59375.2023.10305961

Index Terms

Securing Federated GANs: Enabling Synthetic Data Generation for Health Registry Consortiums

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cover image ACM Other conferences

ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security

August 2023

1440 pages

ISBN:9798400707728

DOI:10.1145/3600160

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2023

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ARES 2023

ARES 2023: The 18th International Conference on Availability, Reliability and Security

August 29 - September 1, 2023

Benevento, Italy

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Cited By

Iantorno MGarza J(2024)Leveraging Artificial Intelligence Models Using Synthetic DataProceedings of the Future Technologies Conference (FTC) 2024, Volume 110.1007/978-3-031-73110-5_4(56-66)Online publication date: 5-Nov-2024
https://doi.org/10.1007/978-3-031-73110-5_4
Karimireddy SVeeraragavan NElvatun SNygård J(2023)Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC59375.2023.10305961(224-231)Online publication date: 18-Sep-2023
https://doi.org/10.1109/FMEC59375.2023.10305961

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