Artificial intelligence (AI) models are increasingly used in the medical
domain. However, as medical data is highly sensitive, special precautions to
ensure its protection are required. The gold standard for privacy preservation
is the introduction of differential privacy (DP) to model training. Prior work
indicates that DP has negative implications on model accuracy and fairness,
which are unacceptable in medicine and represent a main barrier to the
widespread use of privacy-preserving techniques. In this work, we evaluated the
effect of privacy-preserving training of AI models for chest radiograph
diagnosis regarding accuracy and fairness compared to non-private training. For
this, we used a large dataset (N=193,311) of high quality clinical chest
radiographs, which were retrospectively collected and manually labeled by
experienced radiologists. We then compared non-private deep convolutional
neural networks (CNNs) and privacy-preserving (DP) models with respect to
privacy-utility trade-offs measured as area under the
receiver-operator-characteristic curve (AUROC), and privacy-fairness
trade-offs, measured as Pearson's r or Statistical Parity Difference. We found
that the non-private CNNs achieved an average AUROC score of 0.90 +- 0.04 over
all labels, whereas the DP CNNs with a privacy budget of epsilon=7.89 resulted
in an AUROC of 0.87 +- 0.04, i.e., a mere 2.6% performance decrease compared to
non-private training. Furthermore, we found the privacy-preserving training not
to amplify discrimination against age, sex or co-morbidity. Our study shows
that -- under the challenging realistic circumstances of a real-life clinical
dataset -- the privacy-preserving training of diagnostic deep learning models
is possible with excellent diagnostic accuracy and fairness.

本文旨在通过比较隐私保护性训练和非隐私训练，在胸部放射图诊断方面评估两种方法的准确性和公平性，研究发现，在隐私预算为 7.89 的情况下，DP 卷积神经网络与非私有卷积神经网络相比，在保持较高准确性的前提下，性能仅下降了 2.6% 左右，同时，隐私保护训练并不会加剧针对年龄、性别或共病的歧视。