Adaptive differential private in deep learning
Hao Fei, Gehao Lu, Yaling Luo · 2023
With the continuous development of artificial intelligence, it embodies important value in more and more scenarios, such as speech recognition, recommendation systems, computer vision, etc. The deep learning techniques behind them are built on a large amount of data to learn and extract features from different data. The deep learning techniques behind them are built on a large amount of data, learning from different data and extracting features. To protect data security, differential privacy is a good mechanism which is independent of the background knowledge possessed by the attacker and shows excellent results in privacy protection. In this paper, based on differential privacy and combined with knowledge in deep learning optimizer, We propose Adam-DP to improve the learning efficiency by adapting the learning rate and noise, and the adaptive noise also reduces the impact of noise on the model accuracy to some extent. The experiments show that the method has higher accuracy and learning rate compared with the DPSGD method.