{"as_of":"2026-08-08T03:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7cbebb7dc26282ce52d9e9e51212f51e2572d4ecea21c0ea59a72b1308207154","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:51:47.106739Z","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":"2026-07-03T15:58:38.214080Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-07T05:51:47.106739Z","title":"Composition of differential privacy & privacy amplification by subsampling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07102","last_updated":"2025-06-08T12:14:14Z","snapshot_observed_at":"2026-08-07T05:39:50.992845Z","submitted_at":"2025-06-08T12:14:14Z","title":"Decentralized Optimization with Amplified Privacy via Efficient Communication","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:51:47.106739Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2506.07102"},"observation_digest":"sha256:c3019e50d622df47966d608aca21d281c4b376d58169fec68393502cd2ff681c","observation_id":"bdd4cea1-db09-47f4-b71a-1e46930ddf6e","resolution":{"observed_at":"2026-08-07T05:51:47.106739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-06T21:07:11.170918Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00914","last_updated":"2025-07-01T16:18:29Z","snapshot_observed_at":"2026-08-07T21:43:44.054899Z","submitted_at":"2025-07-01T16:18:29Z","title":"Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications","version":1},"reference_index":151,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:11.170918Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2507.00914"},"observation_digest":"sha256:72058444fe1abf078755ec0cef9ebaea5ac5eef3c91a4ab990c32d17eaf24b1f","observation_id":"1352ffb7-fb9b-4ded-a12d-39934a44b91c","resolution":{"observed_at":"2026-08-06T21:07:11.170918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-06T18:03:14.812822Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09699","last_updated":"2025-07-13T16:20:13Z","snapshot_observed_at":"2026-08-06T17:47:13.040627Z","submitted_at":"2025-07-13T16:20:13Z","title":"Interpreting Differential Privacy in Terms of Disclosure Risk","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T18:03:14.812822Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2507.09699"},"observation_digest":"sha256:fc00231b78150d62cdb6ad63673f1370d7db7a0f46dc721b7a4dd2e50b6b8038","observation_id":"ae4ba253-e91f-452b-8746-6281bc9ee3a1","resolution":{"observed_at":"2026-08-06T18:03:14.812822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-04T13:45:02.268040Z","title":"Composition of differential privacy & privacy amplification by subsampling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.25906","last_updated":"2026-05-29T15:06:03Z","snapshot_observed_at":"2026-08-04T13:44:59.284719Z","submitted_at":"2025-09-30T07:51:06Z","title":"Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T13:45:02.268040Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2509.25906"},"observation_digest":"sha256:c590900165b823db39192f38b7c76ad65a0024119e648459e903138d016c8640","observation_id":"c3aad46a-fa57-44b7-a123-cb1822290752","resolution":{"observed_at":"2026-08-04T13:45:02.268040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-03T10:07:16.643303Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.09029","last_updated":"2026-07-11T13:58:58Z","snapshot_observed_at":"2026-08-04T08:45:47.853150Z","submitted_at":"2026-01-17T00:09:44Z","title":"Fixed-Composition Shuffle Asymptotics in the Full-Support Gaussian Regime","version":6},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T10:07:16.643303Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2602.09029"},"observation_digest":"sha256:64124e40a4124e3d112436fd98b7037601a516de8483c4e18a0d1f5b10fde977","observation_id":"fec12f39-83fa-4deb-b0fc-61d25fb5645b","resolution":{"observed_at":"2026-08-03T10:07:16.643303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":"2210.00597","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-07-03T15:58:38.214080Z","title":"arXiv preprint arXiv:2210.00597 , year=","venue":null,"work_id":"73a9ae1b-cb33-4ee5-acec-a46b2cc60fb1","year":2022},"citing_paper":{"arxiv_id":"2605.23131","last_updated":"2026-05-22T01:09:14Z","snapshot_observed_at":"2026-07-06T23:33:24.215365Z","submitted_at":"2026-05-22T01:09:14Z","title":"When Determinants Are Not Enough: Private Rare Switching","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-25T05:04:26.921107Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2605.23131"},"observation_digest":"sha256:6f286229c7b314e976b3def08888180e22553a169c9bf89cae62d6cd317e6a3b","observation_id":"49db8779-7566-48e4-88e0-4d0178564e46","resolution":{"observed_at":"2026-05-25T05:05:22.478711Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":"2210.00597","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-07-03T15:58:38.214080Z","title":"arXiv preprint arXiv:2210.00597 , year=","venue":null,"work_id":"73a9ae1b-cb33-4ee5-acec-a46b2cc60fb1","year":2022},"citing_paper":{"arxiv_id":"2606.13563","last_updated":"2026-06-11T16:48:35Z","snapshot_observed_at":"2026-08-02T20:29:05.748777Z","submitted_at":"2026-06-11T16:48:35Z","title":"Differentially Private Hierarchical Heavy Hitters","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-27T06:14:10.487037Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2606.13563"},"observation_digest":"sha256:69c8b4082d30c56b45a91735bc4ffaf3eecb7f53194c7661ceedfc41f5db0f50","observation_id":"a146cf6e-b830-4187-aed9-6d096434cfa4","resolution":{"observed_at":"2026-07-03T15:58:38.215425Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00597","snapshot_observed_at":"2026-08-03T02:11:38.512622Z","title":"arXiv preprint arXiv:2210.00597 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29675","last_updated":"2026-07-31T17:55:02Z","snapshot_observed_at":"2026-08-07T05:02:52.081428Z","submitted_at":"2026-07-31T17:55:02Z","title":"Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-03T02:11:38.512622Z"},"links":{"cited_paper":"/paper/2210.00597","citing_paper":"/paper/2607.29675"},"observation_digest":"sha256:dd23300dde901c9bc3d64bacb8b284d85093625bc8bf1fdd089de3f8d8f7774b","observation_id":"7d7fe58a-f7c8-45a8-8b10-b7de87bea6c3","resolution":{"observed_at":"2026-08-03T02:11:38.512622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2210.00597/citation-record","integrity":"/paper/2210.00597/integrity","json":"/paper/2210.00597/citation-record.json","paper":"/paper/2210.00597"},"outbound":[],"paper":{"arxiv_id":"2210.00597","last_updated":"2022-10-26T16:13:14Z","latest_version":4,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-03T10:21:08.508642Z","submitted_at":"2022-10-02T18:22:31Z","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2210.00597."}