Differentially Private Learning with Grouped Gradient Clipping

While deep learning has proved success in many critical tasks by training models from large-scale data, some private information within can be recovered from the released models, leading to the leakage of privacy. To address this problem, this paper presents a differentially private deep learning paradigm to train private models. In the approach, we propose and incorporate a simple operation termed grouped gradient clipping to modulate the gradient weights. We also incorporated the smooth sensitivity mechanism into differentially private deep learning paradigm, which bounds the adding Gaussian noise. In this way, the resulting model can simultaneously provide with strong privacy protection and avoid accuracy degradation, providing a good trade-off between privacy and performance. The theoretic advantages of grouped gradient clipping are well analyzed. Extensive evaluations on popular benchmarks and comparisons with 11 state-of-the-arts clearly demonstrate the effectiveness and genearalizability of our approach.