{"as_of":"2026-08-14T12:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:85b3a2d87a136008cc05dd0272f9c15181226d5218cab5e069bc08121131028d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:54:59.548556Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T09:11:21.112481Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.07134","last_updated":"2021-06-21T11:49:45Z","snapshot_observed_at":"2026-08-05T20:47:18.611048Z","submitted_at":"2020-06-12T12:46:42Z","title":"Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07134","snapshot_observed_at":"2026-08-11T15:54:59.548556Z","title":"Tight approximate differential privacy for discrete-valued mechanisms using fft,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.10612","last_updated":"2024-12-13T23:34:30Z","snapshot_observed_at":"2026-08-13T01:52:06.909853Z","submitted_at":"2024-12-13T23:34:30Z","title":"Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:54:59.548556Z"},"links":{"cited_paper":"/paper/2006.07134","citing_paper":"/paper/2412.10612"},"observation_digest":"sha256:66c9f6c5bba57122a674f253670f7f71fbbe15a129b4552dcd6c83672ab422e9","observation_id":"1f72c801-0b2e-4f2b-a4f0-3a7cac9a4afd","resolution":{"observed_at":"2026-08-11T15:54:59.548556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07134","last_updated":"2021-06-21T11:49:45Z","snapshot_observed_at":"2026-08-05T20:47:18.611048Z","submitted_at":"2020-06-12T12:46:42Z","title":"Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07134","snapshot_observed_at":"2026-08-06T22:52:28.431733Z","title":"Tight approximate differential privacy for discrete-valued mechanisms using fft,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.20981","last_updated":"2025-06-26T03:54:19Z","snapshot_observed_at":"2026-08-08T09:05:30.566209Z","submitted_at":"2025-06-26T03:54:19Z","title":"PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:52:28.431733Z"},"links":{"cited_paper":"/paper/2006.07134","citing_paper":"/paper/2506.20981"},"observation_digest":"sha256:fa8b0c1685c0fc52ff6bf0bc2a121f6fea0abda3a3754afd68df4b8225873a51","observation_id":"2d50adeb-320e-48d8-b401-82530b1f77da","resolution":{"observed_at":"2026-08-06T22:52:28.431733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07134","last_updated":"2021-06-21T11:49:45Z","snapshot_observed_at":"2026-08-05T20:47:18.611048Z","submitted_at":"2020-06-12T12:46:42Z","title":"Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT","version":3},"cited_work":{"arxiv_id":"2006.07134","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07134","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using fft","venue":null,"work_id":"1ab2511f-1b21-465f-8775-52fa67fb6b26","year":2021},"citing_paper":{"arxiv_id":"2605.21780","last_updated":"2026-05-20T22:17:29Z","snapshot_observed_at":"2026-07-06T23:32:10.472558Z","submitted_at":"2026-05-20T22:17:29Z","title":"Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-22T09:10:05.320094Z"},"links":{"cited_paper":"/paper/2006.07134","citing_paper":"/paper/2605.21780"},"observation_digest":"sha256:994f59f09be58bbbdabc15e6348bee4a054cc2c12718648f79bc48ac28fea944","observation_id":"9a28b404-a34b-4734-bb05-84dd07d1dc9b","resolution":{"observed_at":"2026-05-22T09:11:21.115058Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.07134/citation-record","integrity":"/paper/2006.07134/integrity","json":"/paper/2006.07134/citation-record.json","paper":"/paper/2006.07134"},"outbound":[],"paper":{"arxiv_id":"2006.07134","last_updated":"2021-06-21T11:49:45Z","latest_version":3,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-05T20:47:18.611048Z","submitted_at":"2020-06-12T12:46:42Z","title":"Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2006.07134."}