{"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-variational-inference","title":"Differentially Private Variational Inference for Non-conjugate Models","arxiv_id":"1610.08749","date":"2016-10-27","proceeding":null,"authors":["Joonas Jälkö","Onur Dikmen","Antti Honkela"],"abstract":"Many machine learning applications are based on data collected from people,\nsuch as their tastes and behaviour as well as biological traits and genetic\ndata. Regardless of how important the application might be, one has to make\nsure individuals' identities or the privacy of the data are not compromised in\nthe analysis. Differential privacy constitutes a powerful framework that\nprevents breaching of data subject privacy from the output of a computation.\nDifferentially private versions of many important Bayesian inference methods\nhave been proposed, but there is a lack of an efficient unified approach\napplicable to arbitrary models. In this contribution, we propose a\ndifferentially private variational inference method with a very wide\napplicability. It is built on top of doubly stochastic variational inference, a\nrecent advance which provides a variational solution to a large class of\nmodels. We add differential privacy into doubly stochastic variational\ninference by clipping and perturbing the gradients. The algorithm is made more\nefficient through privacy amplification from subsampling. We demonstrate the\nmethod can reach an accuracy close to non-private level under reasonably strong\nprivacy guarantees, clearly improving over previous sampling-based alternatives\nespecially in the strong privacy regime.","url_abs":"http://arxiv.org/abs/1610.08749v2","url_pdf":"http://arxiv.org/pdf/1610.08749v2.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-variational-inference","repo_url":"https://github.com/DPBayes/d3p","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"differentially-private-variational-inference","repo_url":"https://github.com/DPBayes/dppp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"variational-inference","task_name":"Variational 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}