{"as_of":"2026-08-10T02:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f0769cae0eba9f28062650048e4daffee298289cb2f63ebbb2bf0529e70409ad","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:31:33.917677Z","state":"measured"},{"denominator":72,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":72,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2505.17092/citation-record","integrity":"/paper/2505.17092/integrity","json":"/paper/2505.17092/citation-record.json","paper":"/paper/2505.17092"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:31:34.591590Z","title":"SecureML: A system for scalable privacy-preserving machine learning,","venue":null,"work_id":"a1c6fa1b-0edc-4d70-a853-f517651e5931","year":2017},"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":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.673355Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:73d570c5beab1d9db1d1f69108c9a74a6944afc9ec34cc7197934093e8d9ae91","observation_id":"c4b91da9-5ccc-452a-ba4c-ce8782ff8fc2","resolution":{"observed_at":"2026-08-07T15:31:34.594737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.582115Z","title":"SecureNN: 3-party secure computation for neural network training,","venue":null,"work_id":"72e40fed-4437-4cda-a5bd-25ddeaca1939","year":2019},"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":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.677375Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:d0942946dc381b51a21207b67fac8523858260728f4d9c20d4ffb617237007c9","observation_id":"b60adb3e-2c62-47e5-a902-3dd5995be7b8","resolution":{"observed_at":"2026-08-07T15:31:34.585600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.572378Z","title":"Falcon: Honest- majority maliciously secure framework for private deep learning,","venue":null,"work_id":"a4f3ffac-0f8b-4eae-9b57-e18554f02944","year":2021},"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":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.681394Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:343b003df83385cf6818dc04b3299a2313cb8faa4d004b7ceb4f05a39c8a91f1","observation_id":"4f85e5b2-6dde-40f7-bf00-2f83ed0770e1","resolution":{"observed_at":"2026-08-07T15:31:34.576216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.562146Z","title":"Secure quantized training for deep learning,","venue":null,"work_id":"eea5face-fc48-445e-9477-f3b0848b85f5","year":2022},"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":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.684719Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:60b2fac2fffa425cab0df8d1cd93d15d63efcaff9ff3c63e238d31ab65250a51","observation_id":"ced23595-0e45-4a49-8d01-d00ab6604dd4","resolution":{"observed_at":"2026-08-07T15:31:34.565941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.552742Z","title":"Cryptographically private support vector machines,","venue":null,"work_id":"3f1ca822-0916-4ced-80dc-9fcbfe053a8d","year":2006},"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":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.688680Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:046cb0f992f9835851aa9e9ae877925844087e26af7f8d057552b31ab57a9b66","observation_id":"8dd2c039-0d6d-4633-9b9a-07a6e872f06a","resolution":{"observed_at":"2026-08-07T15:31:34.556668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.543719Z","title":"ABY - A framework for efficient mixed-protocol secure two-party computation,","venue":null,"work_id":"884e42bc-b6d4-4e25-8390-dade6463c9b3","year":2015},"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":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.691809Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:4ca4a39abc386956f5811c98932afa1ea68eec93e8faaec88bb3fcd4c0f923b3","observation_id":"486d749c-e2b2-49c1-aceb-d2b69aaf4ca2","resolution":{"observed_at":"2026-08-07T15:31:34.547237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.534396Z","title":"Cerebro: A platform for multi-party cryptographic collaborative learning,","venue":null,"work_id":"27c8983e-8b34-4dc5-9941-4de03e8b077c","year":2021},"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":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.695637Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:06c4e3bc83551c2a84f106e20e819967599fcd6f5898778a5e267ba484f20b07","observation_id":"10691e4d-9f40-45cb-b75d-05d4d802ac36","resolution":{"observed_at":"2026-08-07T15:31:34.537915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.524354Z","title":"Manticore: Efficient framework for scalable secure multiparty computation protocols,","venue":null,"work_id":"a379ed9a-be4b-47d7-ab4f-f4b6cbef0b60","year":2021},"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":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.698608Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:99524e48323b053f1ddcee3204429c54ddbf08c4227c7e933d4cc2dfaf6a14d9","observation_id":"3648f861-9fc9-496d-a7b7-2c22f4bdf81a","resolution":{"observed_at":"2026-08-07T15:31:34.528293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.514208Z","title":"Private collaborative neural network learning,","venue":null,"work_id":"2edeed5f-5322-443b-adaf-f3194f61b48a","year":2017},"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":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.701436Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:5078871f936d88debd2aa699b90c8021a9da8202d5f6fd226d93df42ae0637a0","observation_id":"9ed4198e-dce5-4c91-968f-eb8cbd59313d","resolution":{"observed_at":"2026-08-07T15:31:34.518183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.504798Z","title":"BOLT: Privacy-preserving, accurate and efficient inference for transformers,","venue":null,"work_id":"10a336f9-ea65-4295-a4d6-dacc4d9d5aca","year":2024},"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":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.704597Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:7c52713870b613b8cb2cb714b0a009578b45b92795df65e974222f2057cf1ea0","observation_id":"9569797b-7619-4941-9de2-b5b2c462cd34","resolution":{"observed_at":"2026-08-07T15:31:34.508566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.495683Z","title":"Iron: Private inference on transformers,","venue":null,"work_id":"936fbf52-75d1-4b52-830e-fb120fa5d0c4","year":2022},"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":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.708430Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:ed36a13368beb0afbf2b4bfc3bec8a2e2dc1c17e71ecdc0cc8cb0cacef6a43f3","observation_id":"5ab52f49-1394-4a0d-b963-4777f8a3a061","resolution":{"observed_at":"2026-08-07T15:31:34.499040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.486578Z","title":"Sharemind: A framework for fast privacy-preserving computations,","venue":null,"work_id":"9863e867-54a4-4ff0-b362-ce7c7ca05bc7","year":2008},"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":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.713036Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:b731cd136455f443b7ff4b594a22001b845030cea4cce9e49cc4a28d9febee1a","observation_id":"e58f399b-59bb-4eb6-addb-8e4e9aecf3de","resolution":{"observed_at":"2026-08-07T15:31:34.489821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.477217Z","title":null,"venue":null,"work_id":"20deadbc-008a-4c0b-aa59-ba8c19af0fc4","year":2023},"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":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.716004Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:0910aa6dcb767be5631c7adebb2c041efa0d14cae536bd137faa674a5d405c16","observation_id":"a02adeac-cc92-497f-a6e9-ad7bdb95657d","resolution":{"observed_at":"2026-08-07T15:31:34.480467Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:31:34.468034Z","title":"Mpyc: Multiparty computation in python,","venue":null,"work_id":"3cf62ea6-ef12-4ea3-8b55-0c9dbe87f418","year":2023},"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":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.723623Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:c0e885c65796ef131ead439accd7b35f8eac973840aebadf3ca4f146b056e532","observation_id":"a4101bc9-d5c7-45a2-90ba-0867ee7fa9be","resolution":{"observed_at":"2026-08-07T15:31:34.471239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.458280Z","title":"CryptGPU: Fast privacy-preserving machine learning on the GPU,","venue":null,"work_id":"24806ed3-2773-4be2-b8e7-9b29462acb6b","year":2021},"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":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.732921Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:7e59725edf6e9d073e1c54ada2434c4202bd5d669be6861a8bcab0aef6579e9f","observation_id":"68a65ff4-6f16-475e-bca5-58452c13ac8d","resolution":{"observed_at":"2026-08-07T15:31:34.462249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.448941Z","title":"Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,","venue":null,"work_id":"b86eca8c-131e-4908-aaca-c46c6bde454f","year":2020},"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":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.736853Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:9be5d624799e83b67feb2c2bf6472626be62b83d3130c4e9842c69de44fb9e14","observation_id":"d428cac5-1a0a-4bfe-9051-2920dcd6e0cd","resolution":{"observed_at":"2026-08-07T15:31:34.452842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.439424Z","title":"Auditing differentially private machine learning: How private is private sgd?","venue":null,"work_id":"26fe332d-6903-482e-a626-9521c5ae41a1","year":2020},"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":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.739743Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:93edfeb50f5a37a2dda2072aa62828a243953e6709fba20a93ee39e006851dee","observation_id":"94f2b2a4-3edb-4402-891b-a7aed82e382d","resolution":{"observed_at":"2026-08-07T15:31:34.443372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.429572Z","title":"Adversary instantiation: Lower bounds for differentially private machine learning,","venue":null,"work_id":"a46c743a-8749-4ca7-b31f-dcac5644d519","year":2021},"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":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.742609Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:ccf951fe9ac8a224a2d7c64f872178f0c8c029b75a83c02ffb54a4350b693adb","observation_id":"e549c80a-fa65-4b6f-a597-3a1e4533d5ee","resolution":{"observed_at":"2026-08-07T15:31:34.432786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.419655Z","title":"Security against covert adversaries: Efficient protocols for realistic adversaries,","venue":null,"work_id":"29096d8c-66cc-4b75-85ca-8df68997198c","year":2007},"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":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.745609Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:7c3f958e5fa025bb2385b291ffef32707e9cad1ac5bdffef755c0b9d9efbde5b","observation_id":"b6c0b8d2-4189-401b-a2ea-47576c011753","resolution":{"observed_at":"2026-08-07T15:31:34.423107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.410368Z","title":"CrypTFlow: Secure TensorFlow inference,","venue":null,"work_id":"b8305532-79ab-40eb-819f-92346d9331fb","year":2020},"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":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.748698Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:3c33a1408b93c0d9b2e4ea840e010f44a8fe312dadd161f3628734b6c0721a63","observation_id":"a9ea0a51-f8c6-4710-87ac-34858ae64120","resolution":{"observed_at":"2026-08-07T15:31:34.413938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.400862Z","title":"Truncation untangled: Scaling fixed-point arithmetic for privacy-preserving machine learning to large models and datasets,","venue":null,"work_id":"d0f92cba-7f56-4bc7-ae86-8a97bb18f31e","year":2024},"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":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.751813Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:3a2afad4121ef05c7ecbccfabfcc5e7b0700e094f0d1e96553e0e6b54b34a392","observation_id":"60cfa606-416a-4dbe-8b12-a4bd791d0add","resolution":{"observed_at":"2026-08-07T15:31:34.404564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.391043Z","title":"Circuits resilient to additive attacks with applications to secure computation,","venue":null,"work_id":"16e90d4b-dbdf-4d89-be40-1dbf8728317a","year":2014},"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":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.755078Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:048bb88a1be78ca006ec6ab45fed2eac2b542a58c541f6120dc322da3c63a66e","observation_id":"ef108fb1-0ca9-47e1-9f1b-81371befa5b2","resolution":{"observed_at":"2026-08-07T15:31:34.394569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.381377Z","title":"Exploiting machine learning to subvert your spam filter","venue":null,"work_id":"1246a1ae-f04a-41ac-bd75-0826fd4a4cc8","year":2008},"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":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.758244Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:1bf2409e7d83a73be64cc576c1fd22facb9d2773f4328e7752c97660e655e88e","observation_id":"6b94eb1f-b012-4d11-b2d8-89af0c741cfb","resolution":{"observed_at":"2026-08-07T15:31:34.384756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:33.761111Z","title":"How to backdoor federated learning,","venue":null,"work_id":null,"year":2020},"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":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.761111Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:ec3250021b605fa4f876f44853423e1b02b043fa21f24bd365826dc3acc50fa4","observation_id":"4e73269f-ec8b-416c-b6bc-9f4dc465b5ba","resolution":{"observed_at":"2026-08-07T15:31:33.761111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1206.6389","last_updated":"2013-03-25T10:16:36Z","snapshot_observed_at":"2026-07-06T02:50:47.096675Z","submitted_at":"2012-06-27T19:59:59Z","title":"Poisoning Attacks against Support Vector Machines","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.6389","snapshot_observed_at":"2026-08-07T15:31:33.764136Z","title":"Poisoning attacks against support vector machines,","venue":null,"work_id":null,"year":2012},"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":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.764136Z"},"links":{"cited_paper":"/paper/1206.6389","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:e891ea5f6a3f5be3839658b1e1489fbaff9b69855e0a1787dc1c06bf928b997c","observation_id":"138c3c66-26ce-4228-b8e0-dbc2219a71e0","resolution":{"observed_at":"2026-08-07T15:31:33.764136Z","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":"2026-08-07T15:31:34.366501Z","title":"Manipulating ma- chine learning: Poisoning attacks and countermeasures for regression learning,","venue":null,"work_id":"3136af6f-d83b-45b9-a8e8-2ca834723eca","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":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.768592Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:05bbec5b545d98714c770e56ba8f1448d61b8cfcea440fc1162c60d38430e43c","observation_id":"b9b98251-71ab-4c18-9a4a-3ae4641bf21d","resolution":{"observed_at":"2026-08-07T15:31:34.370326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.06733","last_updated":"2019-03-11T20:45:33Z","snapshot_observed_at":"2026-07-06T05:56:16.413472Z","submitted_at":"2017-08-22T17:31:54Z","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.06733","snapshot_observed_at":"2026-08-07T15:31:33.771537Z","title":"Badnets: Identifying vulnerabilities in the machine learning model supply chain,","venue":null,"work_id":null,"year":2017},"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":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.771537Z"},"links":{"cited_paper":"/paper/1708.06733","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:7c856f3b50af9e997f629c5e27a2eee4dba77e63b8acc01c8832acb59244eb3b","observation_id":"e76f7c75-3919-47e4-8cdd-687b8ce4b8d2","resolution":{"observed_at":"2026-08-07T15:31:33.771537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-07T15:31:33.774904Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning,","venue":null,"work_id":null,"year":2017},"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":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.774904Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:0c83abc8047b1771810904822c18fd37fa6d1770ab327a7874b5a21d951753ec","observation_id":"b652768f-63c7-4b92-b203-847035e57fd8","resolution":{"observed_at":"2026-08-07T15:31:33.774904Z","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":"2026-08-07T15:31:34.357385Z","title":"When does machine learning FAIL? Generalized transferability for evasion and poisoning attacks,","venue":null,"work_id":"e15443ef-5c50-4266-b227-9c0ecdf3f4f1","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":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.778461Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:3ecee78fc92538a0a646464dc7d81eda74af4abd60f5b30d53a4b36df5a49e2d","observation_id":"4c577fb7-88d7-4818-ac60-ff4dfdb99cf4","resolution":{"observed_at":"2026-08-07T15:31:34.360435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.348182Z","title":"Poison frogs! targeted clean-label poisoning attacks on neural networks,","venue":null,"work_id":"ed0b1714-eabe-40ea-9ca4-ca9c9666466d","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":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.781713Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:3a1f283d29bfc95a7fd8a0c65c568cc36f58c4d709d5bfb483d9d726acb2bc7d","observation_id":"912afcb9-6949-4e46-b177-aff98fcd25da","resolution":{"observed_at":"2026-08-07T15:31:34.351573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.339532Z","title":"Subpopulation data poisoning at- tacks,","venue":null,"work_id":"2d82b485-0df8-4f81-a8ae-7aa6eaa35ef7","year":2021},"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":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.784803Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:1ab989148824b98965912a8784f076cdb8fc2a47e86705ffee5a3f45fc2f0ceb","observation_id":"3ae0291f-9cdf-49e4-b95f-0e8c3fe60889","resolution":{"observed_at":"2026-08-07T15:31:34.342735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.330327Z","title":"Membership inference attacks against machine learning models,","venue":null,"work_id":"1f75542e-a480-4814-91d2-ea577d7542ef","year":2017},"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":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.788384Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:6f8c0b9b53e45175de099da165457576f6d5615e3498ec448094b0bcf13689b3","observation_id":"39fa9741-88d0-49a1-bfe0-3df9dfe90a4a","resolution":{"observed_at":"2026-08-07T15:31:34.333569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.321317Z","title":"Privacy risk in machine learning: Analyzing the connection to overfitting,","venue":null,"work_id":"a3801aa6-2a17-4422-8c61-e9244ea0da6d","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":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.791442Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:8e3ad007c21af312aad0eeaad86be9e96efde1129814ec295d60808e31ac504b","observation_id":"356e62f1-1d66-4132-a485-632121dfbc26","resolution":{"observed_at":"2026-08-07T15:31:34.324440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:33.794302Z","title":"Membership inference attacks from first principles,","venue":null,"work_id":null,"year":2022},"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":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.794302Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:9116d26bb840d2eed26c239b849ef54b215a7db746f2f1c94d7a2a26d311cd22","observation_id":"a7bb0f7d-3fc5-47dd-9cf7-a29986088b61","resolution":{"observed_at":"2026-08-07T15:31:33.794302Z","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":"2026-08-07T15:31:34.307118Z","title":"Enhanced membership inference attacks against machine learning models,","venue":null,"work_id":"0043c33f-b6a2-4011-a057-12a6dc0a0e53","year":2022},"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":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.797409Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:21ec19236cd94eeaeda22fc876d83df4f2e043a7d97e53a96e12db4ac29358e4","observation_id":"6ec4523b-2367-49b5-b01b-c97570fbfa34","resolution":{"observed_at":"2026-08-07T15:31:34.310447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.298050Z","title":"Extracting training data from large language models,","venue":null,"work_id":"be83f9f6-ffa2-431a-a83d-b2b365d36c29","year":2021},"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":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.800463Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:a2499d6459e69eb7ab2ac0a68512d05a8e2048f9c409ec8453592d833cd9bbd4","observation_id":"219e884d-09b5-4503-b4c1-088f5119afa9","resolution":{"observed_at":"2026-08-07T15:31:34.301567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.289475Z","title":"Reconstructing training data with informed adversaries,","venue":null,"work_id":"44a44519-e5a3-484a-b4b4-b187ff37de13","year":2022},"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":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.803644Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:bb8ead1b0972488a7edba43f486b4482b87b57827f5775aa36bfea2a11e3001e","observation_id":"c7946920-f8a5-4a24-861a-0f3d11dd396f","resolution":{"observed_at":"2026-08-07T15:31:34.292520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00133","last_updated":"2023-06-02T14:48:38Z","snapshot_observed_at":"2026-07-06T15:36:11.529142Z","submitted_at":"2023-05-31T19:18:24Z","title":"A Note On Interpreting Canary Exposure","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00133","snapshot_observed_at":"2026-08-07T15:31:33.806480Z","title":"A note on interpreting canary exposure,","venue":null,"work_id":null,"year":2023},"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":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.806480Z"},"links":{"cited_paper":"/paper/2306.00133","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:8be5837cd7abfbc4915a3da95f88b82aa746b734c92e7859c0ed24694ef76371","observation_id":"f1b5eac1-ee3a-44a2-8455-0a3b5e8e351c","resolution":{"observed_at":"2026-08-07T15:31:33.806480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03928","last_updated":"2023-07-08T08:02:47Z","snapshot_observed_at":"2026-07-06T15:51:43.130122Z","submitted_at":"2023-07-08T08:02:47Z","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","version":1},"cited_work":{"arxiv_id":"2307.03928","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.03928","snapshot_observed_at":"2026-08-07T15:31:33.975214Z","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","venue":"cs.CR","work_id":"10c816c5-0b54-49ca-88e2-be8913a8f2d5","year":2023},"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":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.809751Z"},"links":{"cited_paper":"/paper/2307.03928","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:4cfd5d59483d3220aec18f137ff4acb7f397368e9d526572a71f8754bf74ca33","observation_id":"cec99cc9-1c7a-411c-80c2-4f36f3e7326e","resolution":{"observed_at":"2026-08-07T15:31:33.978883Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.279921Z","title":"Muse: Secure inference resilient to malicious clients,","venue":null,"work_id":"f2f9dd70-2f95-4a5f-b9c5-d9f4d7833f56","year":2021},"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":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.813728Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:af203fe402c683c047789493bbe62154d480f551abc07904944feb2267106377","observation_id":"019cd2f7-7aca-4c43-a101-091dcd39cbe8","resolution":{"observed_at":"2026-08-07T15:31:34.283393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.270160Z","title":"SafeNet: Mitigating data poisoning attacks on private machine learning,","venue":null,"work_id":"3c0fe855-d9ee-4e57-979e-5666db5ac5e2","year":2022},"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":43,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.817172Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:7f47c3eea2c9ab21159133b78c9a8f2b880f3ad96bcce27b190a0c35b7029b92","observation_id":"735913f9-e2d3-4dad-aa54-37ac38f391ba","resolution":{"observed_at":"2026-08-07T15:31:34.273848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.260748Z","title":"Improved primitives for MPC over mixed arithmetic-binary circuits,","venue":null,"work_id":"472003dd-20f6-40e9-956e-e2293c341799","year":2020},"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":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.819918Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:a2d5ef5fe4ab4bf9f273a73669a3e78a9471075270a9cf7e38f55f8a8d00674c","observation_id":"d4beb931-9b53-4571-ad10-8eda2119b2e1","resolution":{"observed_at":"2026-08-07T15:31:34.264206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.250483Z","title":"ABY3: A mixed protocol framework for machine learning,","venue":null,"work_id":"c53472a1-416c-48ae-9f98-ced9de5786a4","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":45,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.823352Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:1878889c72f9d0991561c397932ec8290fd389fbded7639a1dbd8b8631607a60","observation_id":"cfe8db6f-6248-423c-974b-06175b61ca83","resolution":{"observed_at":"2026-08-07T15:31:34.253673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.240414Z","title":"Benchmarking privacy preserving scientific operations,","venue":null,"work_id":"e0711d93-f434-41cd-875d-0baf54dc8a9b","year":2019},"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":46,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.826624Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:0c44b902448a41365592be7d8a8735f8c6473debe25a9787a22c11804ba33efb","observation_id":"a6bce3bb-c846-4da2-a534-40f213de7b21","resolution":{"observed_at":"2026-08-07T15:31:34.244211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.230814Z","title":"Property inference attacks on fully connected neural networks using permutation invariant representations,","venue":null,"work_id":"b1372625-02b3-4cd1-8ee6-f90d6c542ba7","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":47,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.830081Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:0842277fa473b4e9b89df423890689ffce61953ce36af339b72d9c79a92b698d","observation_id":"b7e50a9a-38c0-4d62-b652-eef7713bdf22","resolution":{"observed_at":"2026-08-07T15:31:34.234166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.221609Z","title":"Reverse-engineering deep ReLU networks,","venue":null,"work_id":"50ee51b6-271b-4e2c-ab9c-7f4097628966","year":2020},"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":48,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.833490Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:57266fdcc88ce9689a2374d580a8b838cc6ea0b7f24c2e0faace8a2e743b87b7","observation_id":"d9988844-cd85-43a5-81b1-5122a36a7238","resolution":{"observed_at":"2026-08-07T15:31:34.224884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.211691Z","title":"High accuracy and high fidelity extraction of neural networks,","venue":null,"work_id":"0a51c798-0114-42de-afbf-87e13c4dde8d","year":2020},"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":49,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.836364Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:f50870a2f62fa3409a14d5ec8fbb962891360dae35e9cd5b843759b37279017d","observation_id":"513240e1-35fb-44e0-af07-df74f0c88499","resolution":{"observed_at":"2026-08-07T15:31:34.215506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:33.839522Z","title":"JAX: composable transformations of Python+NumPy programs,","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":50,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.839522Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:ac34e854de55b1d9579e8bb8f97fd097ad3d46d6098afe9152e1c3b35461b248","observation_id":"f1f0940a-3cdb-40cb-8ce0-3c0af77514c7","resolution":{"observed_at":"2026-08-07T15:31:33.839522Z","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":"2026-08-07T15:31:34.197073Z","title":"Analyzing federated learning through an adversarial lens,","venue":null,"work_id":"fba15dc8-4c6a-402b-9e3b-5345fb127953","year":2019},"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":51,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.843214Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:638e71e195a66abcb887646f667e52c748ead645c442963145de873940203dce","observation_id":"7e748bfb-d370-4ccf-87cb-825dda94fd86","resolution":{"observed_at":"2026-08-07T15:31:34.200779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04089","last_updated":"2023-03-31T15:27:01Z","snapshot_observed_at":"2026-08-03T22:12:27.993418Z","submitted_at":"2022-12-08T05:50:53Z","title":"Editing Models with Task Arithmetic","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04089","snapshot_observed_at":"2026-08-07T15:31:33.846382Z","title":"Editing models with task arithmetic,","venue":null,"work_id":null,"year":2022},"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":52,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.846382Z"},"links":{"cited_paper":"/paper/2212.04089","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:f2241c37f8958c53a41cf910db5a3c9769256b774af302809aa22667e5cf29a0","observation_id":"a249a716-6c75-45a0-8c27-7f08081c183e","resolution":{"observed_at":"2026-08-07T15:31:33.846382Z","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":"2026-08-07T15:31:34.188205Z","title":"Handcrafted backdoors in deep neural networks,","venue":null,"work_id":"35397d12-b6ed-4bdc-99e9-71ec87215ab3","year":2022},"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":53,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.850023Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:233059c1a83237ec8c8517015ef5cb648608f4a65a7e38edadf4d7bb57e8bc25","observation_id":"731f19f2-cf08-4c0b-ace0-adf18ae35ac8","resolution":{"observed_at":"2026-08-07T15:31:34.191414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.178063Z","title":"When the curious abandon honesty: Federated learning is not private,","venue":null,"work_id":"978b1902-91f6-4eb1-aec9-bf8f8d355bf5","year":2023},"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":54,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.852962Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:3e93efa5074ca766a87ee1de92ec2702d6917af68084417e355e3338153c0f83","observation_id":"04a4bea5-950d-4173-995f-75cc564e33de","resolution":{"observed_at":"2026-08-07T15:31:34.181554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03350","last_updated":"2024-01-10T23:40:39Z","snapshot_observed_at":"2026-08-02T11:19:37.680728Z","submitted_at":"2023-02-07T09:48:29Z","title":"To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods","version":2},"cited_work":{"arxiv_id":"2302.03350","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.03350","snapshot_observed_at":"2026-08-07T15:31:33.953112Z","title":"To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods","venue":"cs.SE","work_id":"4e7ee504-4a13-45b9-84ad-a8f586c07b96","year":2023},"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":55,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.855961Z"},"links":{"cited_paper":"/paper/2302.03350","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:355d336c5c76338019a419933b79b512428cc8b5ce19b83f531e9b8304b123a9","observation_id":"dfe2941c-29bf-495d-be89-e08640249fb9","resolution":{"observed_at":"2026-08-07T15:31:33.958032Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:33.859113Z","title":"Poisoning attacks on algorithmic fairness,","venue":null,"work_id":null,"year":2020},"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":56,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.859113Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:e560b1a3a888795c8b00421fa9a96bf019ce6ce7ff090272f1d071d326518ef2","observation_id":"89b2313d-2346-461e-9e9d-35e295b821fc","resolution":{"observed_at":"2026-08-07T15:31:33.859113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08669","last_updated":"2020-06-15T18:17:44Z","snapshot_observed_at":"2026-07-06T09:29:21.603604Z","submitted_at":"2020-06-15T18:17:44Z","title":"On Adversarial Bias and the Robustness of Fair Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08669","snapshot_observed_at":"2026-08-07T15:31:33.862634Z","title":"On adversarial bias and the robustness of fair machine learning,","venue":null,"work_id":null,"year":2006},"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":57,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.862634Z"},"links":{"cited_paper":"/paper/2006.08669","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:6bd01af37c46b13c3d8847d18547768931481b3220d27ccc267cb27a7af2777e","observation_id":"02911bd7-6b57-4b95-9f30-a350a458689a","resolution":{"observed_at":"2026-08-07T15:31:33.862634Z","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":"2026-08-07T15:31:34.163711Z","title":"Amplifying membership exposure via data poisoning,","venue":null,"work_id":"c17f3185-c021-4ae2-9859-13f9674c66b3","year":2022},"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":58,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.866067Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:5ee9454ecb2f2aa9b3c5619344e0ae332a4bad6c7fa3d63ee203ec5121493634","observation_id":"cc189feb-b6d8-4156-8339-50d2cf354a19","resolution":{"observed_at":"2026-08-07T15:31:34.167418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.154937Z","title":"Truth serum: Poisoning machine learning models to reveal their secrets,","venue":null,"work_id":"da0c0826-584d-4c26-855d-e83cb8aacae3","year":2022},"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":59,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.869156Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:093e87cbcbb72f6fe4ea9a80d2bf761c2470a59c02331fc3dd317557435ce60f","observation_id":"b9f0f6b9-dd35-4122-b4d1-8a129eaded99","resolution":{"observed_at":"2026-08-07T15:31:34.158411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.145182Z","title":"The privacy onion effect: Memorization is relative,","venue":null,"work_id":"048eca6d-b17d-4750-9cbb-7a5575d83999","year":2022},"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":60,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.872105Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:fc70b6c0e913f1f356ab9bc2456c24abe2f57cf5bd1dd01da5b284de0ea156c9","observation_id":"4ba1f057-44f2-43ff-aa83-9cf75ca075e7","resolution":{"observed_at":"2026-08-07T15:31:34.149160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.135887Z","title":"Cryptography secure against related-key attacks and tampering,","venue":null,"work_id":"caee0e23-d689-4839-8c6c-908c2d81c305","year":2011},"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":61,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.875188Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:82c75fcf073e4239d738e84e23754e9596e953e8e2f90dcb07d5c959610c2097","observation_id":"3476df3d-dcad-4843-9c4e-3578bfcafc8c","resolution":{"observed_at":"2026-08-07T15:31:34.139624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.126564Z","title":"Related-key security for pseudorandom functions beyond the linear barrier,","venue":null,"work_id":"6fa40600-0bc2-4633-8f98-93c7bc7709cb","year":2014},"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":62,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.878103Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:85cf77f9aa6fa46158d135bf9acbcae4162eaf1cfdfb6d5569efd0528f8f6f33","observation_id":"be7ddfc7-748f-459c-a075-f7e22cd6c199","resolution":{"observed_at":"2026-08-07T15:31:34.130329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.116779Z","title":"Learning representations by back- propagating errors,","venue":null,"work_id":"ff26533d-fb2e-4dbb-b726-f55320b85f37","year":1986},"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":63,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.881346Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:708a6ce92ad577c9500a10513f2223315ecba07441b85ae130a46b19eeb272d9","observation_id":"a97a416b-3ff9-4a51-a75b-d0b7a813c9bc","resolution":{"observed_at":"2026-08-07T15:31:34.120465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.107251Z","title":"Protocol:","venue":null,"work_id":"5e2db141-63a6-4b74-8b9b-3a8b252f595f","year":null},"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":65,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.885353Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:20b94e56170a17bb399a814ada68a1b33a275273c6770f9e14e12c1d46d31b97","observation_id":"d1bff0d8-18c2-4544-9504-abd8063991bd","resolution":{"observed_at":"2026-08-07T15:31:34.110229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.089215Z","title":null,"venue":null,"work_id":"8c06b702-a85a-4128-8815-9042821ac438","year":null},"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":67,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.891392Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:687f044cd070f450a5197dc3d7287bfaacd7a4aed3ae7730c7d568c141f14732","observation_id":"26581aa4-b564-463d-8de7-efcb0bb3c66a","resolution":{"observed_at":"2026-08-07T15:31:34.092539Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.069892Z","title":"Figure 5: Parameter Transfer Attack from Section 5.1 Neuron Override Input:Trigger δ, data mean µ, mean scaling α, target neuron index j","venue":null,"work_id":"b0b355ce-c8c7-4534-b813-6394e038bdea","year":null},"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":69,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.898573Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:719dee0a0cd20869175b2dd613f8e0aabb9aa133d4dfdf0a5799811cc651ca70","observation_id":"b4051e93-88a9-4528-ba83-6cf2c718aa67","resolution":{"observed_at":"2026-08-07T15:31:34.073492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.060459Z","title":"Figure 6: Neuron Override Attack from Section 5.2 Scaling for Reconstruction Attacks Input:Attacker protocol inputs: Attacker target class (one hot vector) y, scaling strength C","venue":null,"work_id":"e79227b1-62a1-47d5-abcf-ee391bf55d72","year":null},"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":70,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.902502Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:534b8c6d38e2d5b963a1809ab6d9298fdefec4058b957c05b9b3fed975420fef","observation_id":"d32147f2-898d-49e8-b98b-00a2245b24e7","resolution":{"observed_at":"2026-08-07T15:31:34.063727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.098000Z","title":null,"venue":null,"work_id":"029ccadf-0cc9-4a77-ad50-3957f8ee7226","year":null},"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":71,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.905842Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:36b170f23fd8c297a13fb7a7cbc07209cf2771bf9b0289265eb06c8a047337a4","observation_id":"0db1ac4c-7faf-4255-a5c8-16c22b79fce4","resolution":{"observed_at":"2026-08-07T15:31:34.101340Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.050776Z","title":null,"venue":null,"work_id":"ff8b76f4-d5b3-46eb-b0c8-b42c3aa6c2a4","year":null},"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":72,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.908724Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:334db36ea7353bbb47f83e56f924252716057d00fa35403102a85829f65a796a","observation_id":"257db303-4076-4d67-98d9-8e1e7a20ac3a","resolution":{"observed_at":"2026-08-07T15:31:34.054397Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.079569Z","title":null,"venue":null,"work_id":"abd1dd31-20e8-4728-ac49-15dd3ac568a3","year":null},"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":73,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.911625Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:43930eba9bf77ab83c0b9338bae94ac993618be2ee232ae26016f255d2c0aa41","observation_id":"5158db18-5528-4361-b55c-b028596dea96","resolution":{"observed_at":"2026-08-07T15:31:34.083429Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.041263Z","title":"After Protocol (Reconstruction):","venue":null,"work_id":"82732306-baec-4fd3-a7b5-4d55f1adb3c5","year":null},"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":74,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.914807Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:23ee497760b152b22cede7efdc8d61dc0b24085a5a00d9dcbf0269f04d6aa46b","observation_id":"f16e7b26-dcf8-4924-b4bf-2df1b32c81cd","resolution":{"observed_at":"2026-08-07T15:31:34.045164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:31:34.031905Z","title":"flip” parameters will flip the results of comparisons made in the MPC sigmoid. To flip only one element of a batch, the corresponding “flip","venue":null,"work_id":"35ddebfe-4c4e-45cf-8d02-e36b9a043204","year":null},"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":75,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.917677Z"},"links":{"citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:1b2404fc2912f20abcab8a38d0d0b857e05b5715aa82e1dc7a7c677bb3ff7e81","observation_id":"1850e35f-8ee6-4c74-83af-81dfd9d723a5","resolution":{"observed_at":"2026-08-07T15:31:34.035526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00984","last_updated":"2022-09-15T23:52:36Z","snapshot_observed_at":"2026-07-06T11:43:44.140228Z","submitted_at":"2021-09-02T14:36:55Z","title":"CrypTen: Secure Multi-Party Computation Meets Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00984","snapshot_observed_at":"2026-08-07T15:31:33.729910Z","title":"Available: https://arxiv.org/abs/2109.00984","venue":null,"work_id":null,"year":null},"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":2021,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:33.729910Z"},"links":{"cited_paper":"/paper/2109.00984","citing_paper":"/paper/2505.17092"},"observation_digest":"sha256:b0fd5090e4f71ef4ce503d9761b09ba24d0b657ccd0141417434d6241d374e4d","observation_id":"b5eb256f-252c-4a77-8907-7e97d0ad4976","resolution":{"observed_at":"2026-08-07T15:31:33.729910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.17092","last_updated":"2025-05-21T00:46:45Z","latest_version":1,"primary_category":"cs.CR","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"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":53},"total_outbound_references":72},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2505.17092."}