  Unsupervised domain adaptation (UDA) generally aligns the unlabeled target
domain data to the distribution of the source domain to mitigate the
distribution shift problem. The standard UDA requires sharing the source data
with the target, having potential data privacy leaking risks. To protect the
source data's privacy, we first propose to share the source feature
distribution instead of the source data. However, sharing only the source
feature distribution may still suffer from the membership inference attack who
can infer an individual's membership by the black-box access to the source
model. To resolve this privacy issue, we further study the under-explored
problem of privacy-preserving domain adaptation and propose a method with a
novel differential privacy training strategy to protect the source data
privacy. We model the source feature distribution by Gaussian Mixture Models
(GMMs) under the differential privacy setting and send it to the target client
for adaptation. The target client resamples differentially private source
features from GMMs and adapts on target data with several state-of-art UDA
backbones. With our proposed method, the source data provider could avoid
leaking source data privacy during domain adaptation as well as reserve the
utility. To evaluate our proposed method's utility and privacy loss, we apply
our model on a medical report disease label classification task using two noisy
challenging clinical text datasets. The results show that our proposed method
can preserve source data's privacy with a minor performance influence on the
text classification task.
