{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FTGLJWEUDWRZQNT3Q5NB7CTY6B","short_pith_number":"pith:FTGLJWEU","schema_version":"1.0","canonical_sha256":"2cccb4d8941da398367b875a1f8a78f0697a1a48984224d824b77cd63b9cc300","source":{"kind":"arxiv","id":"2601.02947","version":1},"attestation_state":"computed","paper":{"title":"Quality Degradation Attack in Synthetic Data","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Dong Liu, Mohammad Khalil, Pedro P. Vergara Barrios, Qinyi Liu, Sam Urmian","submitted_at":"2026-01-06T11:43:31Z","abstract_excerpt":"Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the recipient of the released synthetic data and attempts to infer sensitive information from it. This study investigates quality degradation attacks initiated by adversaries who possess access to the real dataset or control over the generation process, such as the data owner, the synthetic data provider, or potential intruders. We formalize a corresponding threat model and empirically evaluate the effectiveness of targeted"},"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":"2601.02947","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CR","submitted_at":"2026-01-06T11:43:31Z","cross_cats_sorted":[],"title_canon_sha256":"0086198c9280f4250d8f0b3ed6320e2c76bc153f7b518475de52c64eca6ff035","abstract_canon_sha256":"5ed947fc3bd235f8df2cd3a54fe433077bc49c64eb1f4b652051169739d512f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T21:04:02.223344Z","signature_b64":"Jz4uG08zWfZq/m2TBG/XNadJKqbiBOXdVeRSVV+BQ8PJb+BNyViaIIikrIoJKGInpaU60ok1xa0anL0yGTs1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cccb4d8941da398367b875a1f8a78f0697a1a48984224d824b77cd63b9cc300","last_reissued_at":"2026-05-21T21:04:02.222677Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T21:04:02.222677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quality Degradation Attack in Synthetic Data","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Dong Liu, Mohammad Khalil, Pedro P. Vergara Barrios, Qinyi Liu, Sam Urmian","submitted_at":"2026-01-06T11:43:31Z","abstract_excerpt":"Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the recipient of the released synthetic data and attempts to infer sensitive information from it. This study investigates quality degradation attacks initiated by adversaries who possess access to the real dataset or control over the generation process, such as the data owner, the synthetic data provider, or potential intruders. We formalize a corresponding threat model and empirically evaluate the effectiveness of targeted"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02947","kind":"arxiv","version":1},"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/2601.02947/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":"2601.02947","created_at":"2026-05-21T21:04:02.222764+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.02947v1","created_at":"2026-05-21T21:04:02.222764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02947","created_at":"2026-05-21T21:04:02.222764+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTGLJWEUDWRZ","created_at":"2026-05-21T21:04:02.222764+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTGLJWEUDWRZQNT3","created_at":"2026-05-21T21:04:02.222764+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTGLJWEU","created_at":"2026-05-21T21:04:02.222764+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B","json":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B.json","graph_json":"https://pith.science/api/pith-number/FTGLJWEUDWRZQNT3Q5NB7CTY6B/graph.json","events_json":"https://pith.science/api/pith-number/FTGLJWEUDWRZQNT3Q5NB7CTY6B/events.json","paper":"https://pith.science/paper/FTGLJWEU"},"agent_actions":{"view_html":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B","download_json":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B.json","view_paper":"https://pith.science/paper/FTGLJWEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.02947&json=true","fetch_graph":"https://pith.science/api/pith-number/FTGLJWEUDWRZQNT3Q5NB7CTY6B/graph.json","fetch_events":"https://pith.science/api/pith-number/FTGLJWEUDWRZQNT3Q5NB7CTY6B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B/action/storage_attestation","attest_author":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B/action/author_attestation","sign_citation":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B/action/citation_signature","submit_replication":"https://pith.science/pith/FTGLJWEUDWRZQNT3Q5NB7CTY6B/action/replication_record"}},"created_at":"2026-05-21T21:04:02.222764+00:00","updated_at":"2026-05-21T21:04:02.222764+00:00"}