  Privacy recently emerges as a severe concern in deep learning, that is,
sensitive data must be prohibited from being shared with the third party during
deep neural network development. In this paper, we propose Morphed Learning
(MoLe), an efficient and secure scheme to deliver deep learning data. MoLe has
two main components: data morphing and Augmented Convolutional (Aug-Conv)
layer. Data morphing allows data providers to send morphed data without privacy
information, while Aug-Conv layer helps deep learning developers to apply their
networks on the morphed data without performance penalty. MoLe provides
stronger security while introducing lower overhead compared to GAZELLE (USENIX
Security 2018), which is another method with no performance penalty on the
neural network. When using MoLe for VGG-16 network on CIFAR dataset, the
computational overhead is only 9% and the data transmission overhead is 5.12%.
As a comparison, GAZELLE has computational overhead of 10,000 times and data
transmission overhead of 421,000 times. In this setting, the attack success
rate of adversary is 7.9 x 10^{-90} for MoLe and 2.9 x 10^{-30} for GAZELLE,
respectively.
