Hierarchical Authorization of Convolutional Neural Networks for Multi-User
Yi Luo, Guorui Feng, Xinpeng Zhang · IEEE Signal Processing Letters · 2021
Convolutional neural networks (CNNs) are widely used in many aspects and achieve excellent results. Due to the authorization from different users, we need to consider the right management caused by multi-user. This paper proposes a novel concept of hierarchical authorization of CNNs, and it can help owners control the output results according to accesses. To realize this idea, we refer to differential privacy and use the Laplace mechanism to perturb the output of the model to vary degrees. In experiments, we run on CIFAR-10 and MNIST datasets using ResNet and VGG, and the results show our method has significant grading effects.