{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Z3HNMBNKWOTRYG52GMD7DTWLYU","short_pith_number":"pith:Z3HNMBNK","schema_version":"1.0","canonical_sha256":"ceced605aab3a71c1bba3307f1cecbc52ff3d2408b82c226814dbcc09f3ef176","source":{"kind":"arxiv","id":"2103.06819","version":6},"attestation_state":"computed","paper":{"title":"TAG: Gradient Attack on Transformer-based Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Caiwen Ding, Chao Shang, Hang Liu, Jieren Deng, Ji Li, Sanguthevar Rajasekaran, Yijue Wang","submitted_at":"2021-03-11T17:41:32Z","abstract_excerpt":"Although federated learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training images (gradient leakage) to a third-party in computer vision. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2103.06819","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2021-03-11T17:41:32Z","cross_cats_sorted":[],"title_canon_sha256":"112c9a06f54daba2fdcc89e117264d8bac2cf2ddb58437486c74c7176770bb83","abstract_canon_sha256":"6de294cd8be14303eb6dc3f8c3a85b1e494b3116a9a366efe17e6c8525a9e999"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:16:10.594107Z","signature_b64":"omqRqN+b5YWbtLIbdth8kludNMbJE5hymoZqJK6s5nAQo/wMF9pAY4dzPoaI75pDsjOhzSUsqmPFqpjlsOrtCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ceced605aab3a71c1bba3307f1cecbc52ff3d2408b82c226814dbcc09f3ef176","last_reissued_at":"2026-07-05T03:16:10.593567Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:16:10.593567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TAG: Gradient Attack on Transformer-based Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Caiwen Ding, Chao Shang, Hang Liu, Jieren Deng, Ji Li, Sanguthevar Rajasekaran, Yijue Wang","submitted_at":"2021-03-11T17:41:32Z","abstract_excerpt":"Although federated learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training images (gradient leakage) to a third-party in computer vision. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.06819","kind":"arxiv","version":6},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.06819/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2103.06819","created_at":"2026-07-05T03:16:10.593643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.06819v6","created_at":"2026-07-05T03:16:10.593643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.06819","created_at":"2026-07-05T03:16:10.593643+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z3HNMBNKWOTR","created_at":"2026-07-05T03:16:10.593643+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z3HNMBNKWOTRYG52","created_at":"2026-07-05T03:16:10.593643+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z3HNMBNK","created_at":"2026-07-05T03:16:10.593643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.14205","citing_title":"Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU","json":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU.json","graph_json":"https://pith.science/api/pith-number/Z3HNMBNKWOTRYG52GMD7DTWLYU/graph.json","events_json":"https://pith.science/api/pith-number/Z3HNMBNKWOTRYG52GMD7DTWLYU/events.json","paper":"https://pith.science/paper/Z3HNMBNK"},"agent_actions":{"view_html":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU","download_json":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU.json","view_paper":"https://pith.science/paper/Z3HNMBNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.06819&json=true","fetch_graph":"https://pith.science/api/pith-number/Z3HNMBNKWOTRYG52GMD7DTWLYU/graph.json","fetch_events":"https://pith.science/api/pith-number/Z3HNMBNKWOTRYG52GMD7DTWLYU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU/action/storage_attestation","attest_author":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU/action/author_attestation","sign_citation":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU/action/citation_signature","submit_replication":"https://pith.science/pith/Z3HNMBNKWOTRYG52GMD7DTWLYU/action/replication_record"}},"created_at":"2026-07-05T03:16:10.593643+00:00","updated_at":"2026-07-05T03:16:10.593643+00:00"}