Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
This work addresses privacy concerns in deep learning for data-sensitive applications, offering an incremental improvement over existing differential privacy methods.
The paper tackles the problem of preserving differential privacy in deep neural networks by introducing an adaptive Laplace mechanism that injects noise based on feature relevance, achieving independence from training steps and demonstrating effectiveness on MNIST and CIFAR-10 datasets.
In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.