Differential Privacy and Its Applications in Machine Learning: Advances and Practical Implementations

Differential privacy provides a mathematically rigorous and quantifiable notion of privacy that enables high-utility data analysis while protecting individual privacy. This survey discusses recent advances in differential privacy theory, including novel variants and mechanisms, as well as the theoretical foundations and practical implementations of differentially private machine learning.