{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:J3MNMP2PLSJCSIJINVJ53XZ4RU","short_pith_number":"pith:J3MNMP2P","canonical_record":{"source":{"id":"1709.07886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-09-22T18:00:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"023c5d6ea20aa5cd6806dd34183306aea7fae4ab4623ab279f008c4a386ab86f","abstract_canon_sha256":"6091bda1b401c0854b23df67e3a68cc609e522fe436b803c2e21c13d4b783192"},"schema_version":"1.0"},"canonical_sha256":"4ed8d63f4f5c922921286d53dddf3c8d29278722bd5987ce0270e7b97706224b","source":{"kind":"arxiv","id":"1709.07886","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.07886","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"1709.07886v1","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.07886","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"J3MNMP2PLSJC","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"J3MNMP2PLSJCSIJI","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"J3MNMP2P","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:J3MNMP2PLSJCSIJINVJ53XZ4RU","target":"record","payload":{"canonical_record":{"source":{"id":"1709.07886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-09-22T18:00:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"023c5d6ea20aa5cd6806dd34183306aea7fae4ab4623ab279f008c4a386ab86f","abstract_canon_sha256":"6091bda1b401c0854b23df67e3a68cc609e522fe436b803c2e21c13d4b783192"},"schema_version":"1.0"},"canonical_sha256":"4ed8d63f4f5c922921286d53dddf3c8d29278722bd5987ce0270e7b97706224b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:12.625587Z","signature_b64":"4Sob7psB4lcwEeiNa6xrWNcpk/CjUQpMEhEc4GfIM8sU+Iq+TocMG/Z/q6qFvd4bEtEF3udH45I4L30/UsMwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ed8d63f4f5c922921286d53dddf3c8d29278722bd5987ce0270e7b97706224b","last_reissued_at":"2026-05-18T00:34:12.624989Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:12.624989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.07886","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G1hts+7n3W/vbCEUSDHKTtZ2OPMgmg4tC2tWKEA8SPTSyCrQRHxga6Xxhzd1ocv/8kuqvugCZ47uxl61ctQ6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T12:00:28.954279Z"},"content_sha256":"25867220642a6df0b5c7ac88b4feb03619e1acf224d9a742ecd1f374b268cf4a","schema_version":"1.0","event_id":"sha256:25867220642a6df0b5c7ac88b4feb03619e1acf224d9a742ecd1f374b268cf4a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:J3MNMP2PLSJCSIJINVJ53XZ4RU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Learning Models that Remember Too Much","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov","submitted_at":"2017-09-22T18:00:19Z","abstract_excerpt":"Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data.\n  We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.07886","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":""},"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":"2026-05-18T00:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UbAibH28fWuCQkT+Jsfx3KxBKICioucuQCKxErI5/XoQtc877BtxlVZXH4k9mL1FXDa+43gLlOBqBuaImSztAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T12:00:28.954650Z"},"content_sha256":"fdc379c815a57437d6afd131e91881895906aab8c570389c41986ed6db4bd33d","schema_version":"1.0","event_id":"sha256:fdc379c815a57437d6afd131e91881895906aab8c570389c41986ed6db4bd33d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/bundle.json","state_url":"https://pith.science/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/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":"2026-06-07T12:00:28Z","links":{"resolver":"https://pith.science/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU","bundle":"https://pith.science/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/bundle.json","state":"https://pith.science/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3MNMP2PLSJCSIJINVJ53XZ4RU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:J3MNMP2PLSJCSIJINVJ53XZ4RU","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":"6091bda1b401c0854b23df67e3a68cc609e522fe436b803c2e21c13d4b783192","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-09-22T18:00:19Z","title_canon_sha256":"023c5d6ea20aa5cd6806dd34183306aea7fae4ab4623ab279f008c4a386ab86f"},"schema_version":"1.0","source":{"id":"1709.07886","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.07886","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"1709.07886v1","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.07886","created_at":"2026-05-18T00:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"J3MNMP2PLSJC","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"J3MNMP2PLSJCSIJI","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"J3MNMP2P","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:fdc379c815a57437d6afd131e91881895906aab8c570389c41986ed6db4bd33d","target":"graph","created_at":"2026-05-18T00:34:12Z","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"},"paper":{"abstract_excerpt":"Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data.\n  We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of t","authors_text":"Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-09-22T18:00:19Z","title":"Machine Learning Models that Remember Too Much"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.07886","kind":"arxiv","version":1},"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":"sha256:25867220642a6df0b5c7ac88b4feb03619e1acf224d9a742ecd1f374b268cf4a","target":"record","created_at":"2026-05-18T00:34:12Z","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":"6091bda1b401c0854b23df67e3a68cc609e522fe436b803c2e21c13d4b783192","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-09-22T18:00:19Z","title_canon_sha256":"023c5d6ea20aa5cd6806dd34183306aea7fae4ab4623ab279f008c4a386ab86f"},"schema_version":"1.0","source":{"id":"1709.07886","kind":"arxiv","version":1}},"canonical_sha256":"4ed8d63f4f5c922921286d53dddf3c8d29278722bd5987ce0270e7b97706224b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ed8d63f4f5c922921286d53dddf3c8d29278722bd5987ce0270e7b97706224b","first_computed_at":"2026-05-18T00:34:12.624989Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:12.624989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Sob7psB4lcwEeiNa6xrWNcpk/CjUQpMEhEc4GfIM8sU+Iq+TocMG/Z/q6qFvd4bEtEF3udH45I4L30/UsMwAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:12.625587Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.07886","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25867220642a6df0b5c7ac88b4feb03619e1acf224d9a742ecd1f374b268cf4a","sha256:fdc379c815a57437d6afd131e91881895906aab8c570389c41986ed6db4bd33d"],"state_sha256":"6c118e57a8a96696d96721b2413e74c13cab7e4a7bd66987a43c4f7cb0940c48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vlu14lKIhI6t1om45ZtJq60vKkhyG5Y2Fes3zwHjiKpD18xBjFIsqvKjJZJfZ6G2etJdIyoNNmAMr9U0JWEmDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T12:00:28.956570Z","bundle_sha256":"aac8e51d669f1b3dd4f75d3a60579ab414ef3675732220d9e102357b461738fa"}}