{"as_of":"2026-08-10T15:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b05f7a9a667e7526b0063414e2f77df43dd704c18233dd108d5e440119e8fb5","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":"2026-08-10T06:31:04.303077+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":"2026-08-07T15:31:33.719410Z","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":"2026-07-03T06:57:43.193163Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.08130","last_updated":"2018-10-23T08:13:06Z","snapshot_observed_at":"2026-08-08T04:36:16.374417Z","submitted_at":"2018-10-18T16:10:12Z","title":"Private Machine Learning in TensorFlow using Secure Computation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08130","snapshot_observed_at":"2026-08-07T15:31:33.719410Z","title":"Private machine learning in tensorflow using secure computation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.17092","last_updated":"2025-05-21T00:46:45Z","snapshot_observed_at":"2026-08-08T04:36:39.562042Z","submitted_at":"2025-05-21T00:46:45Z","title":"Covert Attacks on Machine Learning Training in Passively Secure MPC","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.719410Z"},"links":{"cited_paper":"/paper/1810.08130","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:b83a22878805b5ca0ab39543a5af23089dcf794e68447517260c4ee22592cf4a","observation_id":"2a89f051-8e44-47bc-82ab-7a9dde0a40b7","resolution":{"observed_at":"2026-08-07T15:31:33.719410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08130","last_updated":"2018-10-23T08:13:06Z","snapshot_observed_at":"2026-08-08T04:36:16.374417Z","submitted_at":"2018-10-18T16:10:12Z","title":"Private Machine Learning in TensorFlow using Secure Computation","version":2},"cited_work":{"arxiv_id":"1810.08130","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.08130","snapshot_observed_at":"2026-07-03T06:57:43.193163Z","title":"Private Machine Learning in TensorFlow using Secure Computation","venue":"cs.CR","work_id":"f6c3227a-2962-497f-b8f2-36904724cb6a","year":2018},"citing_paper":{"arxiv_id":"2606.11416","last_updated":"2026-06-09T20:06:29Z","snapshot_observed_at":"2026-08-04T13:41:02.056778Z","submitted_at":"2026-06-09T20:06:29Z","title":"MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-27T12:22:09.190163Z"},"links":{"cited_paper":"/paper/1810.08130","citing_paper":"/paper/2606.11416"},"observation_digest":"sha256:34f0a43fd2effc39985606c65fcb994cbfc57d4d7a546395eac82a4c8dd52084","observation_id":"bdee733a-e58d-4ea3-bd23-dc1f13af3225","resolution":{"observed_at":"2026-07-03T06:57:43.194614Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1810.08130/citation-record","integrity":"/paper/1810.08130/integrity","json":"/paper/1810.08130/citation-record.json","paper":"/paper/1810.08130"},"outbound":[],"paper":{"arxiv_id":"1810.08130","last_updated":"2018-10-23T08:13:06Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T04:36:16.374417Z","submitted_at":"2018-10-18T16:10:12Z","title":"Private Machine Learning in TensorFlow using Secure Computation"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.08130."}