Deep Learning with Gaussian Differential Privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, SU Wei-Jie · Harvard Data Science Review · 2020
-differential privacy framework allows for a new privacy analysis that improves on the prior analysis [3], which in turn suggests tuning certain parameters of neural networks for a better prediction accuracy without violating the privacy budget. These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems. Python code to calculate the privacy cost for these experiments is publicly available in the TensorFlow Privacy library.