A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning. (arXiv:2301.00252v1 [cs.CR])

Large training data and expensive model tweaking are standard features of
deep learning for images. As a result, data owners often utilize cloud
resources to develop large-scale complex models, which raises privacy concerns.
Existing solutions are either too expensive to be practical or do not
sufficiently protect the confidentiality of data and models. In this paper, we
study and compare novel \emph{image disguising} mechanisms, DisguisedNets and
InstaHide, aiming to achieve a better trade-off among the level of protection
for outsourced …

cloud cloud resources confidential data deep learning features images large privacy protect resources result scale solutions standard study training