{"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/locally-private-bayesian-inference-for-count","title":"Locally Private Bayesian Inference for Count Models","arxiv_id":"1803.08471","date":"2018-03-22","proceeding":null,"authors":["Aaron Schein","Zhiwei Steven Wu","Alexandra Schofield","Mingyuan Zhou","Hanna Wallach"],"abstract":"We present a general method for privacy-preserving Bayesian inference in\nPoisson factorization, a broad class of models that includes some of the most\nwidely used models in the social sciences. Our method satisfies limited\nprecision local privacy, a generalization of local differential privacy, which\nwe introduce to formulate privacy guarantees appropriate for sparse count data.\nWe develop an MCMC algorithm that approximates the locally private posterior\nover model parameters given data that has been locally privatized by the\ngeometric mechanism (Ghosh et al., 2012). Our solution is based on two\ninsights: 1) a novel reinterpretation of the geometric mechanism in terms of\nthe Skellam distribution (Skellam, 1946) and 2) a general theorem that relates\nthe Skellam to the Bessel distribution (Yuan & Kalbfleisch, 2000). We\ndemonstrate our method in two case studies on real-world email data in which we\nshow that our method consistently outperforms the commonly-used naive approach,\nobtaining higher quality topics in text and more accurate link prediction in\nnetworks. On some tasks, our privacy-preserving method even outperforms\nnon-private inference which conditions on the true data.","url_abs":"http://arxiv.org/abs/1803.08471v3","url_pdf":"http://arxiv.org/pdf/1803.08471v3.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":"locally-private-bayesian-inference-for-count","repo_url":"https://github.com/xandaschofield/locally_private_bpf_icml19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08471"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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