{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:KOSRYMJFRLSZFVMXTCC7O4WSIP","short_pith_number":"pith:KOSRYMJF","schema_version":"1.0","canonical_sha256":"53a51c31258ae592d5979885f772d243f4184dc27fcec962a170b3074b8c4366","source":{"kind":"arxiv","id":"1811.12040","version":2},"attestation_state":"computed","paper":{"title":"The Power of The Hybrid Model for Mean Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aleksandra Korolova, Brendan Avent, Yatharth Dubey","submitted_at":"2018-11-29T09:52:17Z","abstract_excerpt":"We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary real-valued distributions with bounded support. When the curator knows the distribution's variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines. We then analytically characterize how the e"},"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":"1811.12040","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-11-29T09:52:17Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"7f49e53403dab24fc9fa02ddda508530894568dfb515f65e2cc842abc5ca0ba9","abstract_canon_sha256":"f4ffe2c51b898782e9e11405a5fa69aa6093919740467c2fa45b01079dbe459f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:34.977572Z","signature_b64":"oodStLLjtbtOr8eMldmxpxGxT65F5QmMX/Iv6vzoJLmLSQMM2mEX2i69TqsxoxkPsYfwrAOA665jVIAadK1mCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53a51c31258ae592d5979885f772d243f4184dc27fcec962a170b3074b8c4366","last_reissued_at":"2026-07-05T01:19:34.977138Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:34.977138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Power of The Hybrid Model for Mean Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"cs.CR","authors_text":"Aleksandra Korolova, Brendan Avent, Yatharth Dubey","submitted_at":"2018-11-29T09:52:17Z","abstract_excerpt":"We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary real-valued distributions with bounded support. When the curator knows the distribution's variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines. We then analytically characterize how the e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.12040","kind":"arxiv","version":2},"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/1811.12040/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":"1811.12040","created_at":"2026-07-05T01:19:34.977208+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.12040v2","created_at":"2026-07-05T01:19:34.977208+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.12040","created_at":"2026-07-05T01:19:34.977208+00:00"},{"alias_kind":"pith_short_12","alias_value":"KOSRYMJFRLSZ","created_at":"2026-07-05T01:19:34.977208+00:00"},{"alias_kind":"pith_short_16","alias_value":"KOSRYMJFRLSZFVMX","created_at":"2026-07-05T01:19:34.977208+00:00"},{"alias_kind":"pith_short_8","alias_value":"KOSRYMJF","created_at":"2026-07-05T01:19:34.977208+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/KOSRYMJFRLSZFVMXTCC7O4WSIP","json":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP.json","graph_json":"https://pith.science/api/pith-number/KOSRYMJFRLSZFVMXTCC7O4WSIP/graph.json","events_json":"https://pith.science/api/pith-number/KOSRYMJFRLSZFVMXTCC7O4WSIP/events.json","paper":"https://pith.science/paper/KOSRYMJF"},"agent_actions":{"view_html":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP","download_json":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP.json","view_paper":"https://pith.science/paper/KOSRYMJF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.12040&json=true","fetch_graph":"https://pith.science/api/pith-number/KOSRYMJFRLSZFVMXTCC7O4WSIP/graph.json","fetch_events":"https://pith.science/api/pith-number/KOSRYMJFRLSZFVMXTCC7O4WSIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP/action/storage_attestation","attest_author":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP/action/author_attestation","sign_citation":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP/action/citation_signature","submit_replication":"https://pith.science/pith/KOSRYMJFRLSZFVMXTCC7O4WSIP/action/replication_record"}},"created_at":"2026-07-05T01:19:34.977208+00:00","updated_at":"2026-07-05T01:19:34.977208+00:00"}