Privacy Enhancement for Cloud-Based Few-Shot Learning. (arXiv:2205.07864v1 [cs.LG])

Requiring less data for accurate models, few-shot learning has shown
robustness and generality in many application domains. However, deploying
few-shot models in untrusted environments may inflict privacy concerns, e.g.,
attacks or adversaries that may breach the privacy of user-supplied data. This
paper studies the privacy enhancement for the few-shot learning in an untrusted
environment, e.g., the cloud, by establishing a novel privacy-preserved
embedding space that preserves the privacy of data and maintains the accuracy
of the model. We examine the …

cloud cloud-based lg privacy