论文标题
弥合差距：差异私人的模棱两可的深度学习，用于医学图像分析
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
论文作者
论文摘要
使用差异隐私（DP）等正式隐私技术的机器学习使人们可以从敏感的医学成像数据中获得有价值的见解，同时有望保护患者隐私，但通常以急剧的隐私 - 实用性权衡。在这项工作中，我们建议使用DP使用可进入的卷积网络进行医学图像分析。它们提高的功能质量和参数效率可产生明显的准确性，从而缩小了隐私 - 效用差距。
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.
