{"paper":{"title":"Near-Optimal Generalized Private Testing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.DS","authors_text":"Anamay Chaturvedi, Jalaj Upadhyay, Monika Henzinger","submitted_at":"2026-05-20T18:06:50Z","abstract_excerpt":"In differential privacy (DP), the generalized private testing problem was introduced by Liu and Talwar (STOC 2019). Given a dataset $X \\in \\mathcal{X}$ and a sequence of black-box $\\varepsilon_t$-DP mechanisms $M_t:\\mathcal{X}\\to\\{+1,-1\\}$, the analyst must accept the first mechanism whose success probability $p_t=\\Pr[M_t(X)=+1]$ exceeds a given threshold $p^*\\in(0,1)$, while achieving DP. Accuracy is measured by the gap between $p^*$ and a rejection threshold $\\bar{p}$, such that with probability $1-\\beta$ for all $t\\geq1$, if $p_t\\leq\\bar{p}$, then $M_t$ is rejected, and if $p_t\\geq p^*$, th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21601","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.21601/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"}