{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VMQAFPI7EMP74TSEB5VZC7NRY6","short_pith_number":"pith:VMQAFPI7","schema_version":"1.0","canonical_sha256":"ab2002bd1f231ffe4e440f6b917db1c7a5097c843fe33a2bca2b0d57883fed6d","source":{"kind":"arxiv","id":"2412.13939","version":1},"attestation_state":"computed","paper":{"title":"Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Alexander D. Zemskov, Jian Cao, Jianjing Zhang, Kenneth A. Loparo, Pan Li, Robert Gao, Runchao Li, Vispi Karkaria, Wei Chen, Xufei Wang, Yao Fu, Ying-Kuan Tsai","submitted_at":"2024-12-18T15:21:53Z","abstract_excerpt":"In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remai"},"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":"2412.13939","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-12-18T15:21:53Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"e5735ab577f8f3026f5c0ed1e90691538a249e5ecda94c02584ccb4d52840ef9","abstract_canon_sha256":"a5f9b4c9a6be0e6cc3d9d2271b19a2f35cc424ba7d0237e902b876a716bbf06f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:11.144282Z","signature_b64":"+4IlbiuV1SmhW4jcdx5eWJkVhNAleaUemHCOZ2slncz9JDruPJFO++5/6pibmfI34IO6u2CtpeCIqM6sdDZnAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab2002bd1f231ffe4e440f6b917db1c7a5097c843fe33a2bca2b0d57883fed6d","last_reissued_at":"2026-07-05T09:51:11.143829Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:11.143829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Alexander D. Zemskov, Jian Cao, Jianjing Zhang, Kenneth A. Loparo, Pan Li, Robert Gao, Runchao Li, Vispi Karkaria, Wei Chen, Xufei Wang, Yao Fu, Ying-Kuan Tsai","submitted_at":"2024-12-18T15:21:53Z","abstract_excerpt":"In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13939","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/2412.13939/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":"2412.13939","created_at":"2026-07-05T09:51:11.143888+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13939v1","created_at":"2026-07-05T09:51:11.143888+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13939","created_at":"2026-07-05T09:51:11.143888+00:00"},{"alias_kind":"pith_short_12","alias_value":"VMQAFPI7EMP7","created_at":"2026-07-05T09:51:11.143888+00:00"},{"alias_kind":"pith_short_16","alias_value":"VMQAFPI7EMP74TSE","created_at":"2026-07-05T09:51:11.143888+00:00"},{"alias_kind":"pith_short_8","alias_value":"VMQAFPI7","created_at":"2026-07-05T09:51:11.143888+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.10337","citing_title":"Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6","json":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6.json","graph_json":"https://pith.science/api/pith-number/VMQAFPI7EMP74TSEB5VZC7NRY6/graph.json","events_json":"https://pith.science/api/pith-number/VMQAFPI7EMP74TSEB5VZC7NRY6/events.json","paper":"https://pith.science/paper/VMQAFPI7"},"agent_actions":{"view_html":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6","download_json":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6.json","view_paper":"https://pith.science/paper/VMQAFPI7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13939&json=true","fetch_graph":"https://pith.science/api/pith-number/VMQAFPI7EMP74TSEB5VZC7NRY6/graph.json","fetch_events":"https://pith.science/api/pith-number/VMQAFPI7EMP74TSEB5VZC7NRY6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6/action/storage_attestation","attest_author":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6/action/author_attestation","sign_citation":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6/action/citation_signature","submit_replication":"https://pith.science/pith/VMQAFPI7EMP74TSEB5VZC7NRY6/action/replication_record"}},"created_at":"2026-07-05T09:51:11.143888+00:00","updated_at":"2026-07-05T09:51:11.143888+00:00"}