Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
This addresses privacy concerns for data providers and developers in deep learning by offering a more efficient and secure alternative to existing methods like GAZELLE, though it appears incremental as it builds on prior work.
The paper tackles the problem of privacy-preserving data delivery for deep learning by proposing MoLe, which reduces computational overhead to 9% and data transmission overhead to 5.12% on VGG-16 with CIFAR, while achieving an attack success rate of 7.9 x 10^{-90}.
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.