Numerical Composition of Differential Privacy

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Sivakanth Gopi
Yin Tat Lee
Lukas Wutschitz


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 \emph{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. (2021) which requires $\tilde{\Omega}(k^{1.5})$ 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.

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How to Cite
Gopi, Sivakanth, Yin Tat Lee, and Lukas Wutschitz. 2024. “Numerical Composition of Differential Privacy”. Journal of Privacy and Confidentiality 14 (1).
TPDP 2022