{"as_of":"2026-08-07T21:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d52814a267cb3a959934270e60154acd507c9f1dc5d9fae511704567918fa2ce","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:10:02.959836Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.11649/citation-record","integrity":"/paper/2507.11649/integrity","json":"/paper/2507.11649/citation-record.json","paper":"/paper/2507.11649"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1602.05629","last_updated":"2023-01-26T22:54:08Z","snapshot_observed_at":"2026-08-01T18:04:48.083997Z","submitted_at":"2016-02-17T23:40:56Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.05629","snapshot_observed_at":"2026-08-06T17:10:00.990228Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:00.990228Z"},"links":{"cited_paper":"/paper/1602.05629","citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:f8c5f9e95b4e850e3e4a9fba2208d2c663ba086ccff1b8e014ff7da1a2628021","observation_id":"106a59d9-2eab-4821-8421-866c1120c154","resolution":{"observed_at":"2026-08-06T17:10:00.990228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12439","last_updated":"2025-07-16T17:27:25Z","snapshot_observed_at":"2026-08-06T16:43:22.238557Z","submitted_at":"2025-07-16T17:27:25Z","title":"A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning","version":1},"cited_work":{"arxiv_id":"2507.12439","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.12439","snapshot_observed_at":"2026-08-06T17:10:04.555129Z","title":"A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning","venue":"cs.LG","work_id":"3fb5da76-48f0-45bd-8d00-e6ad0e4583d5","year":2025},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.067670Z"},"links":{"cited_paper":"/paper/2507.12439","citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:e83abf337fda22f39349ad8af14ba77aff4171c7eec58cc780b06bb1653117b9","observation_id":"21c0bd83-1a64-4d98-aa13-80f57536a19d","resolution":{"observed_at":"2026-08-06T17:10:04.623893Z","resolver_source":"local_arxiv","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":"1812.00910","last_updated":"2020-06-06T18:22:55Z","snapshot_observed_at":"2026-08-06T14:12:08.661534Z","submitted_at":"2018-12-03T17:11:21Z","title":"Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.00910","snapshot_observed_at":"2026-08-06T17:10:01.140177Z","title":"Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.140177Z"},"links":{"cited_paper":"/paper/1812.00910","citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:9906cda82d4a5f0dd54ae78dbe8878bff917aab2f6bd1e786b3c53cb03284feb","observation_id":"aa9cfc92-66ab-4708-b508-886a0a9debf9","resolution":{"observed_at":"2026-08-06T17:10:01.140177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/8835269","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:04.384409Z","title":"Exploiting Unintended Feature Leakage in Collaborative Learning,","venue":null,"work_id":"1b61fbc4-26a7-416d-bd07-120bc4ef96e9","year":2019},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.208922Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:4087fa2a05716729e43cba34e7cbeb3189b9975d1563645e21bfc460aacc9a52","observation_id":"3334c337-9802-448d-8ca0-5a42adb68b8b","resolution":{"observed_at":"2026-08-06T17:10:04.433642Z","resolver_source":"raw_fallback","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":"2405.11580","last_updated":"2024-05-19T15:15:18Z","snapshot_observed_at":"2026-08-06T15:08:38.608490Z","submitted_at":"2024-05-19T15:15:18Z","title":"Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11580","snapshot_observed_at":"2026-08-06T17:10:01.286941Z","title":"Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.286941Z"},"links":{"cited_paper":"/paper/2405.11580","citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:438ac6b6fe26569f1044d8d76e6c212fe1dfad9df201a2fe5abc94bc0f86c118","observation_id":"209d7179-f1b0-4ed9-824c-a0abdf59f85e","resolution":{"observed_at":"2026-08-06T17:10:01.286941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:01.338029Z","title":"Practical Secure Aggregation for Privacy-Preserving Machine Learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.338029Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:aa89fc6aff7d4fea7c9355422c8a6ff02726e6991db210b0104074cc8367862c","observation_id":"187d6758-65fe-46da-be27-96c1db9ebe41","resolution":{"observed_at":"2026-08-06T17:10:01.338029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:06.848487Z","title":"The knowledge complexity of interactive proof-systems,","venue":null,"work_id":"e067e2a0-ea6e-45d8-b0ba-cd3fd60149ad","year":2019},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.416675Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:5e4f57da5754640088cd9f10c905b0e67f109e3bbabaa44466abd2e3a0e14fee","observation_id":"5aa21332-5f38-4951-8e27-c6380c17179a","resolution":{"observed_at":"2026-08-06T17:10:06.949030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:06.681789Z","title":"Circom: A circuit description language for building zero-knowledge applications,","venue":null,"work_id":"cd41487d-8369-4a3b-85e2-4fc5a0a582ee","year":2022},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.540585Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:ea715e45d8fc4e87000e2d15b23de912f995f1e3c76a75e9b4881d7f5a5e69f8","observation_id":"7860c45a-c849-4c35-84c0-306b7113fab7","resolution":{"observed_at":"2026-08-06T17:10:06.774163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:06.432230Z","title":"On the Size of Pairing-Based Non-interactive Arguments,","venue":null,"work_id":"0490c2c7-d879-4a1a-a711-aa5f69e58ad1","year":2016},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.657211Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:673eee2d492c4a0cc80c9f7e75e5465d1afa916c7a557e4d56088ab96da8a316","observation_id":"f40563fa-28b8-418f-a94b-5cbbf0ec1683","resolution":{"observed_at":"2026-08-06T17:10:06.548794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:06.251866Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":"ed86fd48-b881-45f9-824c-4c1e1121ab4f","year":1998},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.723395Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:fcd1e931bf93a912b3701d64d483fd9cccbb131a4a5a1431527cbe960f9ec8d7","observation_id":"38cdc17a-4ea9-495f-822e-c3eb3d0b8ff1","resolution":{"observed_at":"2026-08-06T17:10:06.313275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:06.068291Z","title":"The algorithmic foundations of differential privacy,","venue":null,"work_id":"90490d12-cf4a-48fe-85df-d4aa91780fed","year":2014},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.816269Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:5cb417f39a03e6dbead588fa13c335ecdf508cfd92b647ed450be765473ae029","observation_id":"fa13ded1-ad4b-47a7-ace6-cd8a391fdfb4","resolution":{"observed_at":"2026-08-06T17:10:06.128838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":"4996.32952","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:03.969806Z","title":"SafetyNets,","venue":null,"work_id":"5a900679-482e-4ede-8eea-b223664452d1","year":2017},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.934830Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:99aafd705ab4e18b820719dc55500d15a77b561141aa6df7d07ebc8de5864ea1","observation_id":"0ce38298-7812-4abb-814c-390e5210d112","resolution":{"observed_at":"2026-08-06T17:10:04.011250Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:05.861902Z","title":"{GAZELLE}: A Low Latency Framework for Secure Neural Network Inference,","venue":null,"work_id":"002ae682-bd60-422d-adfa-2a6fc09d96d3","year":2018},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.025679Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:2066c03651a921d90fe57f089e937136b107c61e189cef4b8ac95660c96aa79e","observation_id":"72dca290-290e-409b-8918-9d058cf3cdfd","resolution":{"observed_at":"2026-08-06T17:10:05.946005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":"document/8765347","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:03.749241Z","title":"VerifyNet: Secure and Verifiable Federated Learning,","venue":null,"work_id":"a6bdc173-a5f3-4bfb-9e1c-d4191bee37ed","year":2020},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.094339Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:b08bb6540fdaa445fa8ca75546f8fe79d163c211cf8fed6d4b380e60b86f7efb","observation_id":"79734364-1bea-4e50-8415-6562f1fefc1f","resolution":{"observed_at":"2026-08-06T17:10:03.813223Z","resolver_source":"raw_fallback","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":null,"cited_work":{"arxiv_id":"document/1055750","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:03.547697Z","title":"Auditable and verifiable federated learning based on blockchain-enabled decentralization,","venue":null,"work_id":"5038c45b-3d65-4aeb-af52-69d704612fc9","year":2024},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.163967Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:8ff01aab94735b5d65d23ce53e6393b920f1fe848d4a565d19d7dca1ca5c7a73","observation_id":"bf48378a-f8c5-4da0-80dd-a8a54731ea9c","resolution":{"observed_at":"2026-08-06T17:10:03.604816Z","resolver_source":"raw_fallback","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":null,"cited_work":{"arxiv_id":"document/1063967","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:03.294738Z","title":"Securing Blockchain- based IoT Systems with Physical Unclonable Functions and Zero- Knowledge Proofs,","venue":null,"work_id":"560a1d12-0521-4b7e-a05d-a012c806a4cc","year":2024},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.246019Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:09ec0715453b30b062877918434627875b3749075113a2048f7a3e427888d60a","observation_id":"acbe7844-47e2-4ad6-895f-01f2b82a69c5","resolution":{"observed_at":"2026-08-06T17:10:03.377372Z","resolver_source":"raw_fallback","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:02.333281Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.333281Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:36e066a7a3713cb3af834357a124b629d98afe4b194e6650cd8dae8017edc2ba","observation_id":"fc3153b3-633e-4d7a-85c3-11e2245470f5","resolution":{"observed_at":"2026-08-06T17:10:02.333281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:05.664378Z","title":"iden3/snarkjs,","venue":null,"work_id":"e0d0dbf6-9c31-441f-9256-fde58b69d79a","year":2020},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.449860Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:2fdc6785bd8922469313ef1bcc37c20df410dd2d46838f117449c7a6a8129119","observation_id":"1f38e7a6-0e1c-4d26-a8cc-e7b5870bac91","resolution":{"observed_at":"2026-08-06T17:10:05.733930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:05.424416Z","title":"A public domain dataset for human activity recognition using smartphones","venue":null,"work_id":"ada6ad2c-78ae-4a90-93d3-026e0fa582c5","year":2013},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.520687Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:fe623554657e5a4591af91977861b2d9868b9599dea635d68ae441304387a5e4","observation_id":"934dd2dc-16ac-4094-8a13-00cff7b5d1a8","resolution":{"observed_at":"2026-08-06T17:10:05.539183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1909.06335","last_updated":"2019-09-13T17:26:20Z","snapshot_observed_at":"2026-08-02T11:40:53.964079Z","submitted_at":"2019-09-13T17:26:20Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06335","snapshot_observed_at":"2026-08-06T17:10:02.595635Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.595635Z"},"links":{"cited_paper":"/paper/1909.06335","citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:75d60207d93075f76327471bcf21fdc9c7020b8bc5d2c3eaa9103eb9c2d89d88","observation_id":"81343ad9-4b97-4bcb-a7de-eb8e910e4665","resolution":{"observed_at":"2026-08-06T17:10:02.595635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:05.256548Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"a11e2dfd-2f21-4981-b8ac-5b613e92af8d","year":2021},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.683304Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:228cacc839995a0e29cdb0330620aad7aa46d413b646cb4868070378f0b3597e","observation_id":"2e772e3a-3861-437f-8ca2-dec4945efbb4","resolution":{"observed_at":"2026-08-06T17:10:05.323902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:05.104001Z","title":"Deep leakage from gradients,","venue":null,"work_id":"3126f9bf-b931-4f95-b734-9c6991d35589","year":null},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.769526Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:c3c43d178753a427feff40f091c4f6f81413d7a651076e75932b70470b10fc19","observation_id":"32aadf3a-c875-4c58-93fb-3651859f0967","resolution":{"observed_at":"2026-08-06T17:10:05.171641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:04.783322Z","title":"Poseidon: A new hash function for {Zero-Knowledge} proof systems,","venue":null,"work_id":"c70e7365-7b7c-485d-9960-e12e2c28b6d1","year":2021},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.959836Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:098f404b595d2f4930daa1642aa361d272b5851b6148b6ca383bb33b2051108d","observation_id":"38b7b136-7cb2-4c57-b0f7-1c4fe9a4f990","resolution":{"observed_at":"2026-08-06T17:10:04.847414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:10:04.932868Z","title":"Available: https://proceedings.neurips.cc/paper/2019/ hash/60a6c4002cc7b29142def8871531281a-Abstract.html","venue":null,"work_id":"21f155b6-483b-4425-ae1f-3e217cd1fcea","year":2019},"citing_paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:02.862705Z"},"links":{"citing_paper":"/paper/2507.11649"},"observation_digest":"sha256:420f01f6835b2e0f791acb845d0c198d6564059758c7e7b2692195ced39b478f","observation_id":"9d44789c-8894-467a-9178-85a6ae839c34","resolution":{"observed_at":"2026-08-06T17:10:05.007525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2507.11649","last_updated":"2025-07-18T03:24:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T17:02:36.012505Z","submitted_at":"2025-07-15T18:34:14Z","title":"ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":5,"verified_fuzzy":12},"total_outbound_references":24},"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 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2507.11649."}