{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7EYBHYKKHI343IST54QHADNRVY","short_pith_number":"pith:7EYBHYKK","canonical_record":{"source":{"id":"2311.10448","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T11:03:13Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"c3ad1904938b6c846fab099f65e890f0c0b96c7e026e30a572e2e662db0ec75f","abstract_canon_sha256":"e45ab06012307d825016fd293f696dcf04815535f71cf9ece6dc7470b76130f9"},"schema_version":"1.0"},"canonical_sha256":"f93013e14a3a37cda253ef20700db1ae1d3b6d14ecba160c6178175657fe2151","source":{"kind":"arxiv","id":"2311.10448","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10448","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10448v2","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10448","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_12","alias_value":"7EYBHYKKHI34","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_16","alias_value":"7EYBHYKKHI343IST","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_8","alias_value":"7EYBHYKK","created_at":"2026-07-05T08:12:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7EYBHYKKHI343IST54QHADNRVY","target":"record","payload":{"canonical_record":{"source":{"id":"2311.10448","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T11:03:13Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"c3ad1904938b6c846fab099f65e890f0c0b96c7e026e30a572e2e662db0ec75f","abstract_canon_sha256":"e45ab06012307d825016fd293f696dcf04815535f71cf9ece6dc7470b76130f9"},"schema_version":"1.0"},"canonical_sha256":"f93013e14a3a37cda253ef20700db1ae1d3b6d14ecba160c6178175657fe2151","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:28.716141Z","signature_b64":"uVfvmGDnhG0hilhbCg3UA4rxH9uX4TqKDKmfCiAfDDcq7j8RjTsfe2jEXqeAoSbR+iYs0uu1m44dvs0ZC5VTBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f93013e14a3a37cda253ef20700db1ae1d3b6d14ecba160c6178175657fe2151","last_reissued_at":"2026-07-05T08:12:28.715658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:28.715658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.10448","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:12:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s4eeHP99XqEqrt+dgprGso0RCngYYapKLaP5kICFokGCca6aFbpESC6U8gCDg7MgbyFtL9fNuV3SE0TLB32bBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:54:09.394656Z"},"content_sha256":"8018f528c84c65e6e5438b7bc49d98e2dcb6f4cbb7eaa778d9603c19403d1265","schema_version":"1.0","event_id":"sha256:8018f528c84c65e6e5438b7bc49d98e2dcb6f4cbb7eaa778d9603c19403d1265"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7EYBHYKKHI343IST54QHADNRVY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Jiaeli Shi, John Buford, Kostis Gourgoulias, Najah Ghalyan, Sean Moran","submitted_at":"2023-11-17T11:03:13Z","abstract_excerpt":"Machine learning models trained on sensitive or private data can inadvertently memorize and leak that information. Machine unlearning seeks to retroactively remove such details from model weights to protect privacy. We contribute a lightweight unlearning algorithm that leverages the Fisher Information Matrix (FIM) for selective forgetting. Prior work in this area requires full retraining or large matrix inversions, which are computationally expensive. Our key insight is that the diagonal elements of the FIM, which measure the sensitivity of log-likelihood to changes in weights, contain suffici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10448","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/2311.10448/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:12:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8HFqdHiofeMjbtO7/ca32uRtXH3/EK5kkniSKUWeEmfa0olMl6xT7djEGsKKgyRP/RITozVxGm86zM3+2Al9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:54:09.395223Z"},"content_sha256":"22da8259261e334ca4a06dee9d3ade583526580ef957513dba3e1fd2d9e19b30","schema_version":"1.0","event_id":"sha256:22da8259261e334ca4a06dee9d3ade583526580ef957513dba3e1fd2d9e19b30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7EYBHYKKHI343IST54QHADNRVY/bundle.json","state_url":"https://pith.science/pith/7EYBHYKKHI343IST54QHADNRVY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7EYBHYKKHI343IST54QHADNRVY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-20T02:54:09Z","links":{"resolver":"https://pith.science/pith/7EYBHYKKHI343IST54QHADNRVY","bundle":"https://pith.science/pith/7EYBHYKKHI343IST54QHADNRVY/bundle.json","state":"https://pith.science/pith/7EYBHYKKHI343IST54QHADNRVY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7EYBHYKKHI343IST54QHADNRVY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7EYBHYKKHI343IST54QHADNRVY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e45ab06012307d825016fd293f696dcf04815535f71cf9ece6dc7470b76130f9","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T11:03:13Z","title_canon_sha256":"c3ad1904938b6c846fab099f65e890f0c0b96c7e026e30a572e2e662db0ec75f"},"schema_version":"1.0","source":{"id":"2311.10448","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10448","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10448v2","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10448","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_12","alias_value":"7EYBHYKKHI34","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_16","alias_value":"7EYBHYKKHI343IST","created_at":"2026-07-05T08:12:28Z"},{"alias_kind":"pith_short_8","alias_value":"7EYBHYKK","created_at":"2026-07-05T08:12:28Z"}],"graph_snapshots":[{"event_id":"sha256:22da8259261e334ca4a06dee9d3ade583526580ef957513dba3e1fd2d9e19b30","target":"graph","created_at":"2026-07-05T08:12:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.10448/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning models trained on sensitive or private data can inadvertently memorize and leak that information. Machine unlearning seeks to retroactively remove such details from model weights to protect privacy. We contribute a lightweight unlearning algorithm that leverages the Fisher Information Matrix (FIM) for selective forgetting. Prior work in this area requires full retraining or large matrix inversions, which are computationally expensive. Our key insight is that the diagonal elements of the FIM, which measure the sensitivity of log-likelihood to changes in weights, contain suffici","authors_text":"Jiaeli Shi, John Buford, Kostis Gourgoulias, Najah Ghalyan, Sean Moran","cross_cats":["cs.CR","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T11:03:13Z","title":"DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10448","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8018f528c84c65e6e5438b7bc49d98e2dcb6f4cbb7eaa778d9603c19403d1265","target":"record","created_at":"2026-07-05T08:12:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e45ab06012307d825016fd293f696dcf04815535f71cf9ece6dc7470b76130f9","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-17T11:03:13Z","title_canon_sha256":"c3ad1904938b6c846fab099f65e890f0c0b96c7e026e30a572e2e662db0ec75f"},"schema_version":"1.0","source":{"id":"2311.10448","kind":"arxiv","version":2}},"canonical_sha256":"f93013e14a3a37cda253ef20700db1ae1d3b6d14ecba160c6178175657fe2151","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f93013e14a3a37cda253ef20700db1ae1d3b6d14ecba160c6178175657fe2151","first_computed_at":"2026-07-05T08:12:28.715658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:12:28.715658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uVfvmGDnhG0hilhbCg3UA4rxH9uX4TqKDKmfCiAfDDcq7j8RjTsfe2jEXqeAoSbR+iYs0uu1m44dvs0ZC5VTBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:12:28.716141Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.10448","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8018f528c84c65e6e5438b7bc49d98e2dcb6f4cbb7eaa778d9603c19403d1265","sha256:22da8259261e334ca4a06dee9d3ade583526580ef957513dba3e1fd2d9e19b30"],"state_sha256":"caf9f9f8f6009850783de76dc03c0334c28deeb081987aa8d39e4b62705ad046"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ZNqKByx6E/quoVl/6S2IhfAUcPozcR8OEmyE156U2s79jpYdbJWDv4L3u//sdbGK0I72kzmDlA7mgQHx1oFCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T02:54:09.400359Z","bundle_sha256":"ed08f8ce0756e65aa173bc3d08c3b0cf1732effd79ea65b841e237bed0ab43d5"}}