{"as_of":"2026-08-16T20:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7bb7a6e63948c46d127fc9591d243fe7e2202a5594013bea97744124fecebddd","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:46:55.554181Z","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":"2026-06-30T23:35:07.409058Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-08-15T22:46:55.554181Z","title":"Deep learning with gaussian differential privacy","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.06651","last_updated":"2025-05-10T13:57:57Z","snapshot_observed_at":"2026-08-15T22:34:39.041126Z","submitted_at":"2025-05-10T13:57:57Z","title":"Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T22:46:55.554181Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2505.06651"},"observation_digest":"sha256:27f98c5ecd540f09f0e2ee486b08bd7fdad2d7383c90b292670f4ae2ca8b7f0d","observation_id":"b173630c-3a7e-414f-8fc2-c0eab08ae30c","resolution":{"observed_at":"2026-08-15T22:46:55.554181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":"1911.11607","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-06-30T23:35:07.409058Z","title":null,"venue":null,"work_id":"55c1785c-f0d6-41ce-8be8-2114bfea5318","year":1911},"citing_paper":{"arxiv_id":"2601.10237","last_updated":"2026-08-09T17:52:48Z","snapshot_observed_at":"2026-08-13T23:26:39.797921Z","submitted_at":"2026-01-15T09:50:36Z","title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-16T13:37:50.765735Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2601.10237"},"observation_digest":"sha256:83dea23d4f2a0ea60927134434586d0745a81ea2d2d13e7a454616b438bfb7d2","observation_id":"b9cc8bac-e447-4cb6-bf50-a526f186af0c","resolution":{"observed_at":"2026-05-16T13:37:56.535077Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":"1911.11607","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-06-30T23:35:07.409058Z","title":null,"venue":null,"work_id":"55c1785c-f0d6-41ce-8be8-2114bfea5318","year":1911},"citing_paper":{"arxiv_id":"2605.06259","last_updated":"2026-05-24T15:38:04Z","snapshot_observed_at":"2026-08-12T15:17:36.318244Z","submitted_at":"2026-05-07T13:35:43Z","title":"Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T13:24:42.043525Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2605.06259"},"observation_digest":"sha256:403faf5fbf6b7fa04934e621c9f6f83d784cfc2dacdfaf9b9909fe79e2c69e64","observation_id":"f611b4c0-19fd-4c29-90ea-e321df3050d5","resolution":{"observed_at":"2026-05-11T18:56:06.107933Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":"1911.11607","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-06-30T23:35:07.409058Z","title":null,"venue":null,"work_id":"55c1785c-f0d6-41ce-8be8-2114bfea5318","year":1911},"citing_paper":{"arxiv_id":"2605.06259","last_updated":"2026-05-24T15:38:04Z","snapshot_observed_at":"2026-08-12T15:17:36.318244Z","submitted_at":"2026-05-07T13:35:43Z","title":"Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T23:30:48.823067Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2605.06259"},"observation_digest":"sha256:c3546ef05a45c79f61eb0b3e75960f14eb2202b115b0703313a80435a6addfdd","observation_id":"06bd4d31-d155-4647-96f8-21f4df379612","resolution":{"observed_at":"2026-06-30T23:35:07.410459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-07-14T08:05:47.963985Z","title":"Deep learning with gaussian differential privacy,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10952","last_updated":"2026-07-12T23:09:08Z","snapshot_observed_at":"2026-08-01T18:45:29.022681Z","submitted_at":"2026-07-12T23:09:08Z","title":"Differentially Private Consistent Release of Counting Queries","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T08:05:47.963985Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2607.10952"},"observation_digest":"sha256:f1a510cb8667656fb1de201734e3f00d7aba6de43ca7fbd258befbfea340c2c7","observation_id":"8d35538f-412d-4d3a-b518-963a11379c45","resolution":{"observed_at":"2026-07-14T08:05:47.963985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11607","snapshot_observed_at":"2026-08-15T14:35:55.704285Z","title":"CoRRabs/1911.11607(2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.10521","last_updated":"2026-08-11T05:51:27Z","snapshot_observed_at":"2026-08-16T19:02:51.576511Z","submitted_at":"2026-08-11T05:51:27Z","title":"Synthesizing Probabilistic Saturating Counters with Differentially Private Formal Guarantees","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T14:35:55.704285Z"},"links":{"cited_paper":"/paper/1911.11607","citing_paper":"/paper/2608.10521"},"observation_digest":"sha256:e9461931fd6103ee2bb0184dee3d0aaf6a7e316f69535272c2131e1370aee639","observation_id":"5900aaa6-fdaa-499c-aef2-6c347e8aae40","resolution":{"observed_at":"2026-08-15T14:35:55.704285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1911.11607/citation-record","integrity":"/paper/1911.11607/integrity","json":"/paper/1911.11607/citation-record.json","paper":"/paper/1911.11607"},"outbound":[],"paper":{"arxiv_id":"1911.11607","last_updated":"2020-07-22T16:09:13Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T01:49:45.976527Z","submitted_at":"2019-11-26T15:08:58Z","title":"Deep Learning with Gaussian Differential Privacy"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1911.11607."}