{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X5RIIR3S2SHVWZFXN3J5XCCAFP","short_pith_number":"pith:X5RIIR3S","schema_version":"1.0","canonical_sha256":"bf62844772d48f5b64b76ed3db88402be5802a4bbac26fd8a8e22b9dfdadb216","source":{"kind":"arxiv","id":"2410.13812","version":2},"attestation_state":"computed","paper":{"title":"Private Counterfactual Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG","eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Mohamed Nomeir, Pasan Dissanayake, Sanghamitra Dutta, Sennur Ulukus, Shreya Meel","submitted_at":"2024-10-17T17:45:07Z","abstract_excerpt":"Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of fulfilling this requirement. However, this also poses a threat to the privacy of both the institution that is providing the explanation as well as the user who is requesting it. In this work, we propose multiple schemes inspired by private information retrieval (PIR) techniques which ensure the \\emph{user's privacy} when retrieving counterfactual explanations. We present a scheme whi"},"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":"2410.13812","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2024-10-17T17:45:07Z","cross_cats_sorted":["cs.CR","cs.LG","eess.SP","math.IT"],"title_canon_sha256":"98b8aafacf9b464356f89449a1bd11b0e61c2f0a850d94178bcdfe35225a64e7","abstract_canon_sha256":"e2792bc0e07bf8a87273c79a31fdc5844b2d9d8239964e1977bbe8c678ffb988"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:23.487281Z","signature_b64":"QcgKu8/pja8F3WbywK1jHByyKkschLnQ2MzNPV/2Q2GqkaTMHJ1vyex3u4c+W9kNAdp++20Avd6VUciFNVNnCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf62844772d48f5b64b76ed3db88402be5802a4bbac26fd8a8e22b9dfdadb216","last_reissued_at":"2026-07-05T11:42:23.486855Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:23.486855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Private Counterfactual Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG","eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Mohamed Nomeir, Pasan Dissanayake, Sanghamitra Dutta, Sennur Ulukus, Shreya Meel","submitted_at":"2024-10-17T17:45:07Z","abstract_excerpt":"Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of fulfilling this requirement. However, this also poses a threat to the privacy of both the institution that is providing the explanation as well as the user who is requesting it. In this work, we propose multiple schemes inspired by private information retrieval (PIR) techniques which ensure the \\emph{user's privacy} when retrieving counterfactual explanations. We present a scheme whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13812","kind":"arxiv","version":2},"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/2410.13812/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":"2410.13812","created_at":"2026-07-05T11:42:23.486906+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.13812v2","created_at":"2026-07-05T11:42:23.486906+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13812","created_at":"2026-07-05T11:42:23.486906+00:00"},{"alias_kind":"pith_short_12","alias_value":"X5RIIR3S2SHV","created_at":"2026-07-05T11:42:23.486906+00:00"},{"alias_kind":"pith_short_16","alias_value":"X5RIIR3S2SHVWZFX","created_at":"2026-07-05T11:42:23.486906+00:00"},{"alias_kind":"pith_short_8","alias_value":"X5RIIR3S","created_at":"2026-07-05T11:42:23.486906+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10429","citing_title":"Private Counterfactual Retrieval With Immutable Features","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP","json":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP.json","graph_json":"https://pith.science/api/pith-number/X5RIIR3S2SHVWZFXN3J5XCCAFP/graph.json","events_json":"https://pith.science/api/pith-number/X5RIIR3S2SHVWZFXN3J5XCCAFP/events.json","paper":"https://pith.science/paper/X5RIIR3S"},"agent_actions":{"view_html":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP","download_json":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP.json","view_paper":"https://pith.science/paper/X5RIIR3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.13812&json=true","fetch_graph":"https://pith.science/api/pith-number/X5RIIR3S2SHVWZFXN3J5XCCAFP/graph.json","fetch_events":"https://pith.science/api/pith-number/X5RIIR3S2SHVWZFXN3J5XCCAFP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP/action/storage_attestation","attest_author":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP/action/author_attestation","sign_citation":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP/action/citation_signature","submit_replication":"https://pith.science/pith/X5RIIR3S2SHVWZFXN3J5XCCAFP/action/replication_record"}},"created_at":"2026-07-05T11:42:23.486906+00:00","updated_at":"2026-07-05T11:42:23.486906+00:00"}