Safeguarding personal information is paramount for healthcare data sharing, a
challenging issue without any silver bullet thus far. We study the prospect of
a recent deep-learning advent, dataset condensation (DC), in sharing healthcare
data for AI research, and the results are promising. The condensed data
abstracts original records and irreversibly conceals individual-level knowledge
to achieve a bona fide de-identification, which permits free sharing. Moreover,
the original deep-learning utilities are well preserved in the condensed data
with compressed volume and accelerated model convergences. In PhysioNet-2012, a
condensed dataset of 20 samples can orient deep models attaining 80.3% test AUC
of mortality prediction (versus 85.8% of 5120 original records), an inspiring
discovery generalised to MIMIC-III and Coswara datasets. We also interpret the
inhere privacy protections of DC through theoretical analysis and empirical
evidence. Dataset condensation opens a new gate to sharing healthcare data for
AI research with multiple desirable traits.

本研究研究了数据集压缩（DC）在 AI 研究中分享保健数据的前景，并取得了有希望的结果，数据集压缩通过压缩容量和加速模型收敛，既实现了正确的去隐私化，又保留了原始的深度学习功能，同时保护数据隐私并加速模型收敛，DC 为多项愿望的保健数据共享开启了新的大门。