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.

翻译：使用像差异隐私(DP)这样的正式的隐私保护技术学习机器可以从敏感的医疗成像数据中获取有价值的洞察力,同时保证保护患者隐私,但通常会发生一个私隐-私用交换。 在这项工作中,我们建议使用可控的等同变相网络与DP进行医学图像分析。 它们的特性质量和参数效率的提高可以带来显著的准确性收益,缩小私隐-私用差距。