{"paper":{"title":"Private Lossless Multiple Release","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.CR","authors_text":"Boel Nelson, Joel Daniel Andersson, Lukas Retschmeier, Rasmus Pagh","submitted_at":"2025-05-28T15:10:27Z","abstract_excerpt":"Koufogiannis et al. (2016) showed a $\\textit{gradual release}$ result for Laplace noise-based differentially private mechanisms: given an $\\varepsilon$-DP release, a new release with privacy parameter $\\varepsilon' > \\varepsilon$ can be computed such that the combined privacy loss of both releases is at most $\\varepsilon'$ and the distribution of the latter is the same as a single release with parameter $\\varepsilon'$. They also showed gradual release techniques for Gaussian noise, later also explored by Whitehouse et al. (2022).\n  In this paper, we consider a more general $\\textit{multiple re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22449","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/2505.22449/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"}