Wasserstein Differential Privacy

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving machine learning. However, existing DP frameworks do not satisfy all the conditions for becoming metrics, which prevents them from deriving better basic private properties and leads to exaggerated values on privacy budgets. We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality. We show and prove that WDP has 13 excellent properties, which can be theoretical supports for the better performance of WDP than other DP frameworks. In addition, we derive a general privacy accounting method called Wasserstein accountant, which enables WDP to be applied in stochastic gradient descent (SGD) scenarios containing subsampling. Experiments on basic mechanisms, compositions and deep learning show that the privacy budgets obtained by Wasserstein accountant are relatively stable and less influenced by order. Moreover, the overestimation on privacy budgets can be effectively alleviated. The code is available at https://github.com/Hifipsysta/WDP.

Authors

• Chengyi Yang East China Normal University
• Jiayin Qi Guangzhou University
• Aimin Zhou Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, Shanghai Institute of AI for Education, and School of Computer Science and Technology, East China Normal University

Keywords

• CV: Bias, Fairness & Privacy
• ML: Deep Learning Theory
• ML: Privacy
• PEAI: Privacy & Security