{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EOUTRQ3LYJJRYJSI37LC4RQ7NQ","short_pith_number":"pith:EOUTRQ3L","schema_version":"1.0","canonical_sha256":"23a938c36bc2531c2648dfd62e461f6c36144b509e138a8318c9d825a5ec288e","source":{"kind":"arxiv","id":"2605.28078","version":1},"attestation_state":"computed","paper":{"title":"Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CR","authors_text":"Aras Selvi, Huikang Liu, Wolfram Wiesemann","submitted_at":"2026-05-27T07:32:26Z","abstract_excerpt":"We design a class of additive noise mechanisms that satisfy \\((\\varepsilon, \\delta)\\)-differential privacy (DP) for scalar, real-valued query functions with known sensitivities, with a particular focus on moderate and low-privacy regimes. These mechanisms, which we call \\textit{mixture mechanisms}, are constructed by mixing multiple Gaussian distributions that share the same variance but differ in their means and mixture weights. The resulting distributions can be interpreted as convex combinations of a zero-mean Gaussian (as used in the analytic Gaussian mechanism) and additional Gaussians wh"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.28078","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-27T07:32:26Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"f0108161352ed44d503e662c96cd827b5ba0df30377b7ff2c6ce193c6fba7107","abstract_canon_sha256":"6b8cc25805249c763f4d5c6f35ffbbefdfb90c4ceffc0e6718ecf533a7d81d59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:57.947895Z","signature_b64":"mUEaFKiQ1IOYM26hwpghk5DK8A7Mm5AEP90yxRSVZeJ8rk2PMby8AMn7qfKotHpih9NqimCxi410FLqAYJ2aAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23a938c36bc2531c2648dfd62e461f6c36144b509e138a8318c9d825a5ec288e","last_reissued_at":"2026-05-28T01:04:57.947482Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:57.947482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CR","authors_text":"Aras Selvi, Huikang Liu, Wolfram Wiesemann","submitted_at":"2026-05-27T07:32:26Z","abstract_excerpt":"We design a class of additive noise mechanisms that satisfy \\((\\varepsilon, \\delta)\\)-differential privacy (DP) for scalar, real-valued query functions with known sensitivities, with a particular focus on moderate and low-privacy regimes. These mechanisms, which we call \\textit{mixture mechanisms}, are constructed by mixing multiple Gaussian distributions that share the same variance but differ in their means and mixture weights. The resulting distributions can be interpreted as convex combinations of a zero-mean Gaussian (as used in the analytic Gaussian mechanism) and additional Gaussians wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28078","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/2605.28078/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.28078","created_at":"2026-05-28T01:04:57.947547+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28078v1","created_at":"2026-05-28T01:04:57.947547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28078","created_at":"2026-05-28T01:04:57.947547+00:00"},{"alias_kind":"pith_short_12","alias_value":"EOUTRQ3LYJJR","created_at":"2026-05-28T01:04:57.947547+00:00"},{"alias_kind":"pith_short_16","alias_value":"EOUTRQ3LYJJRYJSI","created_at":"2026-05-28T01:04:57.947547+00:00"},{"alias_kind":"pith_short_8","alias_value":"EOUTRQ3L","created_at":"2026-05-28T01:04:57.947547+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ","json":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ.json","graph_json":"https://pith.science/api/pith-number/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/graph.json","events_json":"https://pith.science/api/pith-number/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/events.json","paper":"https://pith.science/paper/EOUTRQ3L"},"agent_actions":{"view_html":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ","download_json":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ.json","view_paper":"https://pith.science/paper/EOUTRQ3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28078&json=true","fetch_graph":"https://pith.science/api/pith-number/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/graph.json","fetch_events":"https://pith.science/api/pith-number/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/action/storage_attestation","attest_author":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/action/author_attestation","sign_citation":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/action/citation_signature","submit_replication":"https://pith.science/pith/EOUTRQ3LYJJRYJSI37LC4RQ7NQ/action/replication_record"}},"created_at":"2026-05-28T01:04:57.947547+00:00","updated_at":"2026-05-28T01:04:57.947547+00:00"}