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：采用形式化隐私保护技术（如差分隐私）的机器学习，能够在保护患者隐私的前提下从敏感的医学影像数据中提取有价值的信息，但通常面临隐私性与实用性之间的尖锐权衡。本研究提出在医学图像分析中结合差分隐私，使用可操控等变卷积网络。该网络通过提升特征质量与参数效率，显著提高了准确性，从而缩小了隐私-实用性差距。