Numerical Composition of Differential Privacy
This provides a faster method for privacy analysis in machine learning, particularly for algorithms like DP-SGD, though it is incremental as it builds on existing privacy loss random variable concepts.
The paper tackles the problem of efficiently composing differential privacy guarantees, achieving a running time of $ ilde{O}(\sqrt{k})$ to approximate privacy curves, which improves over prior methods requiring $ ilde{\Omega}(k^{1.5})$ and speeds up computations by orders of magnitude.
We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss random variables to quantify the privacy loss of DP algorithms. The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\tilde{O}(\sqrt{k})$. This improves over the best prior method by Koskela et al. (2020) which requires $\tildeΩ(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.