Alapfogalmak
Establishing convergent f-DP bounds using shifted interpolation for noisy optimization algorithms.
Kivonat
The content discusses the application of shifted interpolation for differential privacy analysis in the context of noisy optimization algorithms. It introduces the concept of f-DP and explores the convergence of privacy bounds using this technique. The analysis covers various scenarios such as full-batch, cyclic, and stochastic gradient descent, providing improved privacy guarantees. Theoretical methodology and practical implications are discussed, showcasing the versatility and effectiveness of the approach.
Statisztikák
Noisy gradient descent is the predominant algorithm for differentially private machine learning.
The paper improves privacy analysis by establishing the "privacy amplification by iteration" phenomenon.
Techniques extend to various settings like convex/strongly convex, constrained/unconstrained optimization.
Idézetek
"Noisy gradient descent and its variants are the predominant algorithms for private optimization."
"Our key technical insight is the construction of shifted interpolated processes."