2026-07-22T13:15:19Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25320 2026-03-19T13:03:28Z ddc:004 open_access

Uniformity Testing Under User-Level Local Privacy Canonne, Clément L. Gentle, Abigail Singhal, Vikrant Differential Privacy Local Differential Privacy Uniformity Testing Identity Testing Hypothesis Testing User-Level Differential Privacy Person-Level Differential Privacy We initiate the study of distribution testing under 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 than the usual locally private setting, as for the same parameter ε the privacy guarantee must now apply to a full batch of m data points. While some recent work considers distribution 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Clément L. Canonne and Abigail Gentle and Vikrant Singhal 2026 Is Part Of LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ITCS.2026.33 urn:nbn:de:0030-drops-253201 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.33 eng https://creativecommons.org/licenses/by/4.0/legalcode