{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VPZPVIYFWG7WUAKSHTPPT33N4C","short_pith_number":"pith:VPZPVIYF","schema_version":"1.0","canonical_sha256":"abf2faa305b1bf6a01523cdef9ef6de0afbc1fe7d823737aee62589dbb056873","source":{"kind":"arxiv","id":"2607.08867","version":1},"attestation_state":"computed","paper":{"title":"Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jason Rojas, Jiajie He, Keke Chen, Yash Patel, Yuechun Gu, Zeyun Yu","submitted_at":"2026-07-09T18:43:58Z","abstract_excerpt":"Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development. Image disguising has recently emerged as a promising privacy-enhancing technology (PET) that transforms images into visually unintelligible representations while preserving information for downstream learning. We established a unified framework to evaluate representative methods, DisguisedNets and NeuraCrypt, across four datasets involving classification and semantic segmentation tasks. Our analysis assessed predictive "},"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":"2607.08867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-09T18:43:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6a65782bff09db5835ced6fd0ddf3dfe5590295fc44719b262d2498d666dcb6b","abstract_canon_sha256":"33460e4a1f91f483a44f2897b036bdc02d6018c2234600a1748f0180e50ce6d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T00:17:23.209446Z","signature_b64":"I1D9l+HUCo38Hctmxtkgwp5a3tH0biTcstFjq2LF88zFDuXhDIkZN/xAtHbwM24zi6jmmPdp3pFIATMroBXyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abf2faa305b1bf6a01523cdef9ef6de0afbc1fe7d823737aee62589dbb056873","last_reissued_at":"2026-07-13T00:17:23.208476Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T00:17:23.208476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jason Rojas, Jiajie He, Keke Chen, Yash Patel, Yuechun Gu, Zeyun Yu","submitted_at":"2026-07-09T18:43:58Z","abstract_excerpt":"Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development. Image disguising has recently emerged as a promising privacy-enhancing technology (PET) that transforms images into visually unintelligible representations while preserving information for downstream learning. We established a unified framework to evaluate representative methods, DisguisedNets and NeuraCrypt, across four datasets involving classification and semantic segmentation tasks. Our analysis assessed predictive "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08867","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/2607.08867/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":"2607.08867","created_at":"2026-07-13T00:17:23.208967+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08867v1","created_at":"2026-07-13T00:17:23.208967+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08867","created_at":"2026-07-13T00:17:23.208967+00:00"},{"alias_kind":"pith_short_12","alias_value":"VPZPVIYFWG7W","created_at":"2026-07-13T00:17:23.208967+00:00"},{"alias_kind":"pith_short_16","alias_value":"VPZPVIYFWG7WUAKS","created_at":"2026-07-13T00:17:23.208967+00:00"},{"alias_kind":"pith_short_8","alias_value":"VPZPVIYF","created_at":"2026-07-13T00:17:23.208967+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/VPZPVIYFWG7WUAKSHTPPT33N4C","json":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C.json","graph_json":"https://pith.science/api/pith-number/VPZPVIYFWG7WUAKSHTPPT33N4C/graph.json","events_json":"https://pith.science/api/pith-number/VPZPVIYFWG7WUAKSHTPPT33N4C/events.json","paper":"https://pith.science/paper/VPZPVIYF"},"agent_actions":{"view_html":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C","download_json":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C.json","view_paper":"https://pith.science/paper/VPZPVIYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08867&json=true","fetch_graph":"https://pith.science/api/pith-number/VPZPVIYFWG7WUAKSHTPPT33N4C/graph.json","fetch_events":"https://pith.science/api/pith-number/VPZPVIYFWG7WUAKSHTPPT33N4C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C/action/storage_attestation","attest_author":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C/action/author_attestation","sign_citation":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C/action/citation_signature","submit_replication":"https://pith.science/pith/VPZPVIYFWG7WUAKSHTPPT33N4C/action/replication_record"}},"created_at":"2026-07-13T00:17:23.208967+00:00","updated_at":"2026-07-13T00:17:23.208967+00:00"}