Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

18 Sep 2017NhatHai PhanXintao WuHan HuDejing Dou

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... (read more)

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