Privacy in AI (PAI)

The Microsoft Research Privacy in AI group explores questions related to Privacy-Preserving Machine Learning and Federated Learning. Some of the topics of interest covering research areas in PPML and FL are:

• Privacy attacks, including membership inference, poisoning attacks, and reconstruction attacks
• Privacy metrics and properties of machine learning pipelines
• Privacy mitigations in machine learning, such as differentially private model training
• Causal Federated Learning
• Federated optimization
• Multi-party data collaboration
• Privacy in Federated Learning
• Adversarial robustness in Federated Learning
• Hierarchical training
• Genetic algorithms for Federated Learning