Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis

Authors: Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Published: 2022-09-09 | Updated: 2023-06-20

Source: https://arxiv.org/abs/2209.04338

PDF: https://arxiv.org/pdf/2209.04338

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モデル設計 医療モニタリングシステム DNN IP保護手法

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Abstract

Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.