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Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation by Jiang, Shiyi; Firouzi, Farshad; Chakrabarty, Krishnendu is a scholarly article available to read on EtoBox.

What is Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation about?

The increasing need for sharing healthcare data and collaborating on clinical research has raised privacy concerns. Health information leakage due to malicious attacks can lead to serious problems such as misdiagnoses and patient identification issues. Privacy-preserving machine learning (PPML) and privacy-enhancing technologies, particularly federated learning (FL), have emerged in recent years as innovative solutions to balance privacy protection with data utility; however, they also suffer from inherent privacy vulnerabilities. Gradient inversion attacks constitute major threats to data sharing in federated learning. Researchers have proposed many defenses against gradient inversion attacks. However, current defense methods for healthcare data lack generalizability, i.e., existing solutions may not be applicable to data from a broader range of populations. In addition, most existing defense methods are tested using non-healthcare data, which raises concerns about their applicability to real-world healthcare systems. In this study, we present a defense against gradient inversion attacks in federated learning. We achieve this using latent data perturbation and minimax optimization

Author

Jiang, Shiyi; Firouzi, Farshad; Chakrabarty, Krishnendu

Published

2025

Language

EN