{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KRQSTJOHHP6SZELKRCOK4Q2PY2","short_pith_number":"pith:KRQSTJOH","canonical_record":{"source":{"id":"2311.00919","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T01:25:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"09f562cdc00acfb05bb1f67f7c722a389b18b461115e53b7ca13e06a81ec7bdf","abstract_canon_sha256":"19725cdb6fe1afa4998de1218f30f9dcd80352a233c0dca6bad2312e23d81bcd"},"schema_version":"1.0"},"canonical_sha256":"546129a5c73bfd2c916a889cae434fc68f615ae272eb8b247c3f4421744a15ca","source":{"kind":"arxiv","id":"2311.00919","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00919","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00919v2","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00919","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"KRQSTJOHHP6S","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"KRQSTJOHHP6SZELK","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"KRQSTJOH","created_at":"2026-07-05T08:24:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KRQSTJOHHP6SZELKRCOK4Q2PY2","target":"record","payload":{"canonical_record":{"source":{"id":"2311.00919","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T01:25:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"09f562cdc00acfb05bb1f67f7c722a389b18b461115e53b7ca13e06a81ec7bdf","abstract_canon_sha256":"19725cdb6fe1afa4998de1218f30f9dcd80352a233c0dca6bad2312e23d81bcd"},"schema_version":"1.0"},"canonical_sha256":"546129a5c73bfd2c916a889cae434fc68f615ae272eb8b247c3f4421744a15ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:23.013676Z","signature_b64":"OFeo0/mSzPT/1JrIXBr2l2eECTBlJQroFnxuS9Sj8/+kmEPjZPnlNPx7aONNYorMjUZJybyuoH9PiFdSE0r7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"546129a5c73bfd2c916a889cae434fc68f615ae272eb8b247c3f4421744a15ca","last_reissued_at":"2026-07-05T08:24:23.013207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:23.013207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.00919","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":"2026-07-05T08:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zvE4riyPwvDfrH1FHX3ztoPyKj5vxOGVoDwH/JQb62EJXxFMKZQUJkzmg494ubhb6psnUa/GMCv7aO4bcyq+BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T04:01:29.817201Z"},"content_sha256":"b6b48b521125c56edd3893be58a15396fb7ecfdb5ca0149f00fcfa544bd5a17b","schema_version":"1.0","event_id":"sha256:b6b48b521125c56edd3893be58a15396fb7ecfdb5ca0149f00fcfa544bd5a17b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KRQSTJOHHP6SZELKRCOK4Q2PY2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Bruno Ribeiro, Jiacheng Li, Ninghui Li","submitted_at":"2023-11-02T01:25:49Z","abstract_excerpt":"In Member Inference (MI) attacks, the adversary try to determine whether an instance is used to train a machine learning (ML) model. MI attacks are a major privacy concern when using private data to train ML models. Most MI attacks in the literature take advantage of the fact that ML models are trained to fit the training data well, and thus have very low loss on training instances. Most defenses against MI attacks therefore try to make the model fit the training data less well. Doing so, however, generally results in lower accuracy. We observe that training instances have different degrees of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00919","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/2311.00919/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":"2026-07-05T08:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yPA3z9/nNCc7LRarMo+pzazoFaPCen0rwLlasB1n3T6dTpw1W7WBkMl+d/ijz6WXZ9KNuC9cJ9esqF7OEb46CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T04:01:29.817767Z"},"content_sha256":"08653ecacf3730b54bcaf9f62786aaec8c26008477bebc1c75471e4988f0048d","schema_version":"1.0","event_id":"sha256:08653ecacf3730b54bcaf9f62786aaec8c26008477bebc1c75471e4988f0048d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/bundle.json","state_url":"https://pith.science/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/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":"2026-08-12T04:01:29Z","links":{"resolver":"https://pith.science/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2","bundle":"https://pith.science/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/bundle.json","state":"https://pith.science/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KRQSTJOHHP6SZELKRCOK4Q2PY2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KRQSTJOHHP6SZELKRCOK4Q2PY2","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":"19725cdb6fe1afa4998de1218f30f9dcd80352a233c0dca6bad2312e23d81bcd","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T01:25:49Z","title_canon_sha256":"09f562cdc00acfb05bb1f67f7c722a389b18b461115e53b7ca13e06a81ec7bdf"},"schema_version":"1.0","source":{"id":"2311.00919","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00919","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00919v2","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00919","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"KRQSTJOHHP6S","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"KRQSTJOHHP6SZELK","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"KRQSTJOH","created_at":"2026-07-05T08:24:23Z"}],"graph_snapshots":[{"event_id":"sha256:08653ecacf3730b54bcaf9f62786aaec8c26008477bebc1c75471e4988f0048d","target":"graph","created_at":"2026-07-05T08:24:23Z","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/2311.00919/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In Member Inference (MI) attacks, the adversary try to determine whether an instance is used to train a machine learning (ML) model. MI attacks are a major privacy concern when using private data to train ML models. Most MI attacks in the literature take advantage of the fact that ML models are trained to fit the training data well, and thus have very low loss on training instances. Most defenses against MI attacks therefore try to make the model fit the training data less well. Doing so, however, generally results in lower accuracy. We observe that training instances have different degrees of","authors_text":"Bruno Ribeiro, Jiacheng Li, Ninghui Li","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T01:25:49Z","title":"MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00919","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:b6b48b521125c56edd3893be58a15396fb7ecfdb5ca0149f00fcfa544bd5a17b","target":"record","created_at":"2026-07-05T08:24:23Z","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":"19725cdb6fe1afa4998de1218f30f9dcd80352a233c0dca6bad2312e23d81bcd","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T01:25:49Z","title_canon_sha256":"09f562cdc00acfb05bb1f67f7c722a389b18b461115e53b7ca13e06a81ec7bdf"},"schema_version":"1.0","source":{"id":"2311.00919","kind":"arxiv","version":2}},"canonical_sha256":"546129a5c73bfd2c916a889cae434fc68f615ae272eb8b247c3f4421744a15ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"546129a5c73bfd2c916a889cae434fc68f615ae272eb8b247c3f4421744a15ca","first_computed_at":"2026-07-05T08:24:23.013207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:24:23.013207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OFeo0/mSzPT/1JrIXBr2l2eECTBlJQroFnxuS9Sj8/+kmEPjZPnlNPx7aONNYorMjUZJybyuoH9PiFdSE0r7DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:24:23.013676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.00919","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6b48b521125c56edd3893be58a15396fb7ecfdb5ca0149f00fcfa544bd5a17b","sha256:08653ecacf3730b54bcaf9f62786aaec8c26008477bebc1c75471e4988f0048d"],"state_sha256":"83cc8529465896b696d0996afbba192ed3961e42d8f38765435b281caf466972"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dFEORMxqp80qFxvklJ1oe3fG9KYh7RD/fxjvIPHweZkUb5gPkgOYvZjT8yHZRMb/m8Cms2adWYiIraas5z5hCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T04:01:29.821522Z","bundle_sha256":"6a01aa116213997aba57432706548ac0c5836bbc91ee3ca397fd82504537edb6"}}