{"as_of":"2026-08-11T09:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0082e5d26287999a15631b63122943a894fa4598dac5824e6343392f18467e34","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:18:39.064275Z","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":"2026-05-14T18:17:34.732056Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.10517","last_updated":"2022-11-08T15:06:49Z","snapshot_observed_at":"2026-08-09T05:26:13.440456Z","submitted_at":"2022-02-21T20:16:27Z","title":"Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.10517","snapshot_observed_at":"2026-08-06T21:18:39.064275Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00596","last_updated":"2025-09-10T12:44:37Z","snapshot_observed_at":"2026-08-10T05:42:29.412127Z","submitted_at":"2025-07-01T09:26:38Z","title":"Gaze3P: Gaze-Based Prediction of User-Perceived Privacy","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:18:39.064275Z"},"links":{"cited_paper":"/paper/2202.10517","citing_paper":"/paper/2507.00596"},"observation_digest":"sha256:4face9beebe4d97f45d02377157d038bf1187bbbc760d69f427bb161a74926f2","observation_id":"b7037cca-9980-4c4f-a1d2-0a76f822ff96","resolution":{"observed_at":"2026-08-06T21:18:39.064275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.10517","last_updated":"2022-11-08T15:06:49Z","snapshot_observed_at":"2026-08-09T05:26:13.440456Z","submitted_at":"2022-02-21T20:16:27Z","title":"Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees","version":4},"cited_work":{"arxiv_id":"2202.10517","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.10517","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Individ- ualized pate: Differentially private machine learning with individual privacy guarantees","venue":null,"work_id":"409db750-8cb2-407f-b6db-df17da10842f","year":2022},"citing_paper":{"arxiv_id":"2605.13503","last_updated":"2026-05-13T13:24:50Z","snapshot_observed_at":"2026-07-30T09:41:28.588935Z","submitted_at":"2026-05-13T13:24:50Z","title":"Limits of Personalizing Differential Privacy Budgets","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-14T18:16:54.472287Z"},"links":{"cited_paper":"/paper/2202.10517","citing_paper":"/paper/2605.13503"},"observation_digest":"sha256:465c0cbc10f10b090574dcd647438b82f3e09f3c328e4969c3f3387d9ed12031","observation_id":"39889266-35e6-4e11-a0e1-414f8aac0ffb","resolution":{"observed_at":"2026-05-14T18:17:34.735683Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2202.10517/citation-record","integrity":"/paper/2202.10517/integrity","json":"/paper/2202.10517/citation-record.json","paper":"/paper/2202.10517"},"outbound":[],"paper":{"arxiv_id":"2202.10517","last_updated":"2022-11-08T15:06:49Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T05:26:13.440456Z","submitted_at":"2022-02-21T20:16:27Z","title":"Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.10517."}