How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy
Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang
Abstract
Machine Learning (ML) models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of models' training data.
Topics & Concepts
Differential privacyComputer scienceFocus (optics)Constant (computer programming)Information privacyPrivacy protectionArtificial intelligenceComputer securityData miningProgramming languagePhysicsOpticsPrivacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningCryptography and Data Security