{"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-differentially-private-contextual","title":"Locally Differentially Private (Contextual) Bandits Learning","arxiv_id":"2006.00701","date":"2020-06-01","proceeding":"NeurIPS 2020 12","authors":["Kai Zheng","Tianle Cai","Weiran Huang","Zhenguo Li","Li-Wei Wang"],"abstract":"We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free bandits learning problems with LDP guarantee. Based on our frameworks, we can improve previous best results for private bandits learning with one-point feedback, such as private Bandits Convex Optimization, and obtain the first result for Bandits Convex Optimization (BCO) with multi-point feedback under LDP. LDP guarantee and black-box nature make our frameworks more attractive in real applications compared with previous specifically designed and relatively weaker differentially private (DP) context-free bandits algorithms. Further, we extend our $(\\varepsilon, \\delta)$-LDP algorithm to Generalized Linear Bandits, which enjoys a sub-linear regret $\\tilde{O}(T^{3/4}/\\varepsilon)$ and is conjectured to be nearly optimal. Note that given the existing $\\Omega(T)$ lower bound for DP contextual linear bandits (Shariff & Sheffe, 2018), our result shows a fundamental difference between LDP and DP contextual bandits learning.","url_abs":"https://arxiv.org/abs/2006.00701v4","url_pdf":"https://arxiv.org/pdf/2006.00701v4.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-differentially-private-contextual","repo_url":"https://github.com/huang-research-group/LDPbandit2020","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"locally-differentially-private-contextual","repo_url":"https://github.com/kingcong/models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"locally-differentially-private-contextual","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/rl/ldp_linucb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"},{"task_slug":"privacy-preserving-deep-learning","task_name":"Privacy Preserving Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.00701","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}