{"paper":{"title":"The Price of Differential Privacy For Online Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Karan Singh, Naman Agarwal","submitted_at":"2017-01-27T06:17:14Z","abstract_excerpt":"We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $\\tilde{O}(\\sqrt{T})$ regret bounds. In the full-information setting, our results demonstrate that $\\epsilon$-differential privacy may be ensured for free -- in particular, the regret bounds scale as $O(\\sqrt{T})+\\tilde{O}\\left(\\frac{1}{\\epsilon}\\right)$. For bandit linear optimization, and as a special case, for non-stochastic multi-armed bandits, the proposed algorithm achieves a regret of $\\tilde{O}\\left(\\frac{1}{\\epsilon}\\sqrt{T}\\right)$, while "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07953","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}