[2206.04236] Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition

Computer Science > Cryptography and Security arXiv:2206.04236 (cs) [Submitted on 9 Jun 2022 (v1), last revised 7 Apr 2026 (this version, v3)] Title:Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition Authors:Hua Wang, Sheng Gao, Huanyu Zhang, Milan Shen, Weijie J. Su, Jiayuan Wu View a PDF of the paper titled Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition, by Hua Wang and 5 other authors View PDF HTML (experimental) Abstract:In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to efficiently compute the overall privacy loss under composition. This paper introduces the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees for private algorithms. Leveraging the $f$-differential privacy framework, the Edgeworth Accountant accurately tracks privacy loss under composition, enabling a closed-form expression of privacy guarantees through privacy-loss log-likelihood ratios (PLLRs). As implied by its name, this method applies the Edgeworth expansion to estimate and define the probability distribution of the sum of the PLLRs. Furthermore, by using a technique that simplifies complex distributions into simpler ones, we demonstrate the Edgeworth Accountant's applicability to any noise-addition mechanism. Its main advantage is providing $(\epsilon, \delta)$-differential...