{"ID":2852104,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18379","arxiv_id":"2510.18379","title":"Uniformity Testing under User-Level Local Privacy","abstract":"We initiate the study of distribution testing under \\emph{user-level} local differential privacy, where each of $n$ users contributes $m$ samples from the unknown underlying distribution. This setting, albeit very natural, is significantly more challenging that the usual locally private setting, as for the same parameter $\\varepsilon$ the privacy guarantee must now apply to a full batch of $m$ data points. While some recent work consider distribution \\emph{learning} in this user-level setting, nothing was known for even the most fundamental testing task, uniformity testing (and its generalization, identity testing). We address this gap, by providing (nearly) sample-optimal user-level LDP algorithms for uniformity and identity testing. Motivated by practical considerations, our main focus is on the private-coin, symmetric setting, which does not require users to share a common random seed nor to have been assigned a globally unique identifier.","short_abstract":"We initiate the study of distribution testing under \\emph{user-level} local differential privacy, where each of $n$ users contributes $m$ samples from the unknown underlying distribution. This setting, albeit very natural, is significantly more challenging that the usual locally private setting, as for the same paramet...","url_abs":"https://arxiv.org/abs/2510.18379","url_pdf":"https://arxiv.org/pdf/2510.18379v1","authors":"[\"Clément L. Canonne\",\"Abigail Gentle\",\"Vikrant Singhal\"]","published":"2025-10-21T07:52:41Z","proceeding":"cs.DS","tasks":"[\"cs.DS\",\"cs.CR\",\"cs.DM\"]","methods":"[]","has_code":false}