Secure neuroimaging analysis using federated learning with homomorphic encryption

Dimitris Stripelis; Hamza Saleem; Tanmay Ghai; Nikhil Dhinagar; Umang Gupta; Chrysovalantis Anastasiou; Greg Ver Steeg; Srivatsan Ravi; Muhammad Naveed; Paul M. Thompson; Jose Luis Ambite

doi:10.1117/12.2606256

10 December 2021 Secure neuroimaging analysis using federated learning with homomorphic encryption

Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Author Affiliations +

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208814 (2021) https://doi.org/10.1117/12.2606256
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In this work, we propose a framework for secure FL using fullyhomomorphic encryption (FHE). Specifically, we use the CKKS construction, an approximate, floating point compatible scheme that benefits from ciphertext packing and rescaling. In our evaluation on large-scale brain MRI datasets, we use our proposed secure FL framework to train a deep learning model to predict a person’s age from distributed MRI scans, a common benchmarking task, and demonstrate that there is no degradation in the learning performance between the encrypted and non-encrypted federated models.

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Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, and Jose Luis Ambite "Secure neuroimaging analysis using federated learning with homomorphic encryption", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208814 (10 December 2021); https://doi.org/10.1117/12.2606256

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KEYWORDS

Neuroimaging

Brain

Computer security

Performance modeling

Statistical modeling

Computer programming

Magnetic resonance imaging

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