{"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/differentially-private-bayesian-learning-on","title":"Differentially Private Bayesian Learning on Distributed Data","arxiv_id":"1703.01106","date":"2017-03-03","proceeding":"NeurIPS 2017 12","authors":["Mikko Heikkilä","Eemil Lagerspetz","Samuel Kaski","Kana Shimizu","Sasu Tarkoma","Antti Honkela"],"abstract":"Many applications of machine learning, for example in health care, would\nbenefit from methods that can guarantee privacy of data subjects. Differential\nprivacy (DP) has become established as a standard for protecting learning\nresults. The standard DP algorithms require a single trusted party to have\naccess to the entire data, which is a clear weakness. We consider DP Bayesian\nlearning in a distributed setting, where each party only holds a single sample\nor a few samples of the data. We propose a learning strategy based on a secure\nmulti-party sum function for aggregating summaries from data holders and the\nGaussian mechanism for DP. Our method builds on an asymptotically optimal and\npractically efficient DP Bayesian inference with rapidly diminishing extra\ncost.","url_abs":"http://arxiv.org/abs/1703.01106v2","url_pdf":"http://arxiv.org/pdf/1703.01106v2.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":"differentially-private-bayesian-learning-on","repo_url":"https://github.com/DPBayes/dca-nips2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}