{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/shuffle-gaussian-mechanism-for-differential","title":"Shuffle Gaussian Mechanism for Differential Privacy","arxiv_id":"2206.09569","date":"2022-06-20","proceeding":null,"authors":["Seng Pei Liew","Tsubasa Takahashi"],"abstract":"We study Gaussian mechanism in the shuffle model of differential privacy (DP). Particularly, we characterize the mechanism's R\\'enyi differential privacy (RDP), showing that it is of the form: $$ \\epsilon(\\lambda) \\leq \\frac{1}{\\lambda-1}\\log\\left(\\frac{e^{-\\lambda/2\\sigma^2}}{n^\\lambda}\\sum_{\\substack{k_1+\\dotsc+k_n=\\lambda;\\\\k_1,\\dotsc,k_n\\geq 0}}\\binom{\\lambda}{k_1,\\dotsc,k_n}e^{\\sum_{i=1}^nk_i^2/2\\sigma^2}\\right) $$ We further prove that the RDP is strictly upper-bounded by the Gaussian RDP without shuffling. The shuffle Gaussian RDP is advantageous in composing multiple DP mechanisms, where we demonstrate its improvement over the state-of-the-art approximate DP composition theorems in privacy guarantees of the shuffle model. Moreover, we extend our study to the subsampled shuffle mechanism and the recently proposed shuffled check-in mechanism, which are protocols geared towards distributed/federated learning. Finally, an empirical study of these mechanisms is given to demonstrate the efficacy of employing shuffle Gaussian mechanism under the distributed learning framework to guarantee rigorous user privacy.","url_abs":"https://arxiv.org/abs/2206.09569v2","url_pdf":"https://arxiv.org/pdf/2206.09569v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"shuffle-gaussian-mechanism-for-differential","repo_url":"https://github.com/spliew/shuffgauss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}