Explore the concept of Gaussian Differential Privacy in this 43-minute lecture by 苏炜杰 at BIMSA for #ICBS2024. Delve into the proposed relaxation of differential privacy called "f-DP," which addresses composition issues and offers improved privacy analysis. Learn about the canonical single-parameter family within f-DP known as "Gaussian Differential Privacy" and its significance in privacy-preserving data analysis. Discover the central limit theorem that establishes Gaussian differential privacy as a focal point for hypothesis-testing based privacy definitions under composition. Examine the Edgeworth Accountant, an analytical approach for composing f-DP guarantees of private algorithms. Gain insights into the practical applications of these concepts through an improved analysis of privacy guarantees in noisy stochastic gradient descent.