Privacy Enhancement for Cloud-Based Few-Shot Learning
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by
Archit Parnami, Muhammad Usama, Liyue Fan, Minwoo Lee
2022
Abstract
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 impact of various image privacy methods such as
blurring, pixelization, Gaussian noise, and differentially private pixelization
(DP-Pix) on few-shot image classification and propose a method that learns
privacy-preserved representation through the joint loss. The empirical results
show how privacy-performance trade-off can be negotiated for privacy-enhanced
few-shot learning.
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