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

Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving