Fundamentals of privacy-preserving and secure machine learning

This chapter discusses common threats against the privacy and security of Machine Learning (ML), such as inferring sensitive information from ML models and poisoning deployed models. It also discusses multiple countermeasures to overcome those attacks by focusing in particular on defenses that can be applied at various stages, e.g., during the inference and training phases, or capturing different inputs, e.g., model and data.