A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis. (arXiv:2201.07317v1 [cs.CL])

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 …

analysis arxiv domain adaptation framework privacy text unsupervised