{"as_of":"2026-08-20T16:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ec91f4c0f9f4fe5b5c311c833f521f6080441b7cb1bd176eacec34627cd500cc","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":"2026-08-20T06:33:59.587034+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":"2026-08-15T22:27:03.612852Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T22:27:03.876706Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.02997","last_updated":"2021-03-24T12:47:01Z","snapshot_observed_at":"2026-08-16T18:55:36.297958Z","submitted_at":"2021-01-08T13:12:40Z","title":"Differentially Private Federated Learning for Cancer Prediction","version":2},"cited_work":{"arxiv_id":"2101.02997","doi":null,"metadata_source":"pith","pith_arxiv_id":"2101.02997","snapshot_observed_at":"2026-08-15T22:27:03.876706Z","title":"Differentially Private Federated Learning for Cancer Prediction","venue":"stat.ML","work_id":"19c9e7bc-bbcc-45b3-906e-b04950447e14","year":2021},"citing_paper":{"arxiv_id":"2505.07188","last_updated":"2025-05-12T02:36:50Z","snapshot_observed_at":"2026-08-16T13:40:52.293911Z","submitted_at":"2025-05-12T02:36:50Z","title":"Securing Genomic Data Against Inference Attacks in Federated Learning Environments","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:03.612852Z"},"links":{"cited_paper":"/paper/2101.02997","citing_paper":"/paper/2505.07188"},"observation_digest":"sha256:8e3a49e217be1c29b5ea49187d0da2fe0851a8bfeb8d2251b53fe4309d3de79f","observation_id":"aeb0b675-3c8a-4013-930e-f99ce4e07570","resolution":{"observed_at":"2026-08-15T22:27:03.880844Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.02997","last_updated":"2021-03-24T12:47:01Z","snapshot_observed_at":"2026-08-16T18:55:36.297958Z","submitted_at":"2021-01-08T13:12:40Z","title":"Differentially Private Federated Learning for Cancer Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.02997","snapshot_observed_at":"2026-08-02T19:59:25.374450Z","title":"Beguier, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.13293","last_updated":"2026-06-20T09:46:39Z","snapshot_observed_at":"2026-08-14T12:20:33.774725Z","submitted_at":"2026-02-28T09:36:38Z","title":"A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T19:59:25.374450Z"},"links":{"cited_paper":"/paper/2101.02997","citing_paper":"/paper/2603.13293"},"observation_digest":"sha256:994ae458066da2631f69de86a07cdd328b1893707717c8cabd2eb8b2b80c16e0","observation_id":"f14ddb40-62bd-42ec-a478-743735f6f2dd","resolution":{"observed_at":"2026-08-02T19:59:25.374450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2101.02997/citation-record","integrity":"/paper/2101.02997/integrity","json":"/paper/2101.02997/citation-record.json","paper":"/paper/2101.02997"},"outbound":[],"paper":{"arxiv_id":"2101.02997","last_updated":"2021-03-24T12:47:01Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-16T18:55:36.297958Z","submitted_at":"2021-01-08T13:12:40Z","title":"Differentially Private Federated Learning for Cancer Prediction"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2101.02997."}