{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IJ7LW6UYTUGV3VMVXLYN3QIBMV","short_pith_number":"pith:IJ7LW6UY","canonical_record":{"source":{"id":"1902.08552","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-02-22T16:38:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3531288411013ea72f037673097550a2df4899f0ec030b69452b8db2babbac19","abstract_canon_sha256":"b40e329fb856c468806406dbb0d460e1c27a230bfe70f4af516e5307c5dfacb7"},"schema_version":"1.0"},"canonical_sha256":"427ebb7a989d0d5dd595baf0ddc10165524b28909b56e57cce098c9d4093250b","source":{"kind":"arxiv","id":"1902.08552","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.08552","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"arxiv_version","alias_value":"1902.08552v1","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08552","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"pith_short_12","alias_value":"IJ7LW6UYTUGV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IJ7LW6UYTUGV3VMV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IJ7LW6UY","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IJ7LW6UYTUGV3VMVXLYN3QIBMV","target":"record","payload":{"canonical_record":{"source":{"id":"1902.08552","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-02-22T16:38:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3531288411013ea72f037673097550a2df4899f0ec030b69452b8db2babbac19","abstract_canon_sha256":"b40e329fb856c468806406dbb0d460e1c27a230bfe70f4af516e5307c5dfacb7"},"schema_version":"1.0"},"canonical_sha256":"427ebb7a989d0d5dd595baf0ddc10165524b28909b56e57cce098c9d4093250b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:56.860235Z","signature_b64":"//IlUu/yFrSG9G2APjIpE7XiHPp1RfUS2DxHAHg/uw+p8fQfM+CPQjHa+CjboGDdVhBgwswFRG7aQNbMnuYQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427ebb7a989d0d5dd595baf0ddc10165524b28909b56e57cce098c9d4093250b","last_reissued_at":"2026-05-17T23:52:56.859684Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:56.859684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.08552","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":"2026-05-17T23:52:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XE+OEpWkC2xLpAsLnqa+5q+gRN6oKtpbaY3CFAxclz9sjWDFZu8fPrq1egEV115bXLkbpSWHHBsduk+qNf/lBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:38:06.661936Z"},"content_sha256":"99bdbde348b3b7b472c016df2994c546ce444b7178f081d88b661d919159b670","schema_version":"1.0","event_id":"sha256:99bdbde348b3b7b472c016df2994c546ce444b7178f081d88b661d919159b670"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IJ7LW6UYTUGV3VMVXLYN3QIBMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Ee-Chien Chang, Zhenkai Liang, Ziqi Yang","submitted_at":"2019-02-22T16:38:29Z","abstract_excerpt":"The rise of deep learning technique has raised new privacy concerns about the training data and test data. In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08552","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":"2026-05-17T23:52:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"emmb4uHzbhNIkrbk2qj3OHNdIDiL/fklgwjvh3cWwzHb6Y6IS15+Bpi3UmPkMsMxEA3obbBhGiPYczJXzW6KAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:38:06.662619Z"},"content_sha256":"c355e60d725a6b2d4bd0d8a177abc14101e437094f289f6541caa216b1c0f834","schema_version":"1.0","event_id":"sha256:c355e60d725a6b2d4bd0d8a177abc14101e437094f289f6541caa216b1c0f834"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/bundle.json","state_url":"https://pith.science/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/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":"2026-05-31T21:38:06Z","links":{"resolver":"https://pith.science/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV","bundle":"https://pith.science/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/bundle.json","state":"https://pith.science/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IJ7LW6UYTUGV3VMVXLYN3QIBMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IJ7LW6UYTUGV3VMVXLYN3QIBMV","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":"b40e329fb856c468806406dbb0d460e1c27a230bfe70f4af516e5307c5dfacb7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-02-22T16:38:29Z","title_canon_sha256":"3531288411013ea72f037673097550a2df4899f0ec030b69452b8db2babbac19"},"schema_version":"1.0","source":{"id":"1902.08552","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.08552","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"arxiv_version","alias_value":"1902.08552v1","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08552","created_at":"2026-05-17T23:52:56Z"},{"alias_kind":"pith_short_12","alias_value":"IJ7LW6UYTUGV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IJ7LW6UYTUGV3VMV","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IJ7LW6UY","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:c355e60d725a6b2d4bd0d8a177abc14101e437094f289f6541caa216b1c0f834","target":"graph","created_at":"2026-05-17T23:52:56Z","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":"The rise of deep learning technique has raised new privacy concerns about the training data and test data. In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion ","authors_text":"Ee-Chien Chang, Zhenkai Liang, Ziqi Yang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-02-22T16:38:29Z","title":"Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08552","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":"sha256:99bdbde348b3b7b472c016df2994c546ce444b7178f081d88b661d919159b670","target":"record","created_at":"2026-05-17T23:52:56Z","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":"b40e329fb856c468806406dbb0d460e1c27a230bfe70f4af516e5307c5dfacb7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-02-22T16:38:29Z","title_canon_sha256":"3531288411013ea72f037673097550a2df4899f0ec030b69452b8db2babbac19"},"schema_version":"1.0","source":{"id":"1902.08552","kind":"arxiv","version":1}},"canonical_sha256":"427ebb7a989d0d5dd595baf0ddc10165524b28909b56e57cce098c9d4093250b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"427ebb7a989d0d5dd595baf0ddc10165524b28909b56e57cce098c9d4093250b","first_computed_at":"2026-05-17T23:52:56.859684Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:56.859684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"//IlUu/yFrSG9G2APjIpE7XiHPp1RfUS2DxHAHg/uw+p8fQfM+CPQjHa+CjboGDdVhBgwswFRG7aQNbMnuYQAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:56.860235Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.08552","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99bdbde348b3b7b472c016df2994c546ce444b7178f081d88b661d919159b670","sha256:c355e60d725a6b2d4bd0d8a177abc14101e437094f289f6541caa216b1c0f834"],"state_sha256":"ba60d118cb09ed53b3bc1aa0ca2b6884a40be1685db18f8fca69f4f8d281c8cc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0MXTm10wzvF7F8ohm4Vu4UISk/Zv1oabMBMxyfVJLve5FCghIjoELtHK/LQEjNrTrTJacae6B897ma5cd/HxBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T21:38:06.665886Z","bundle_sha256":"a20abe24d2fd1ec0e3e077e356fa426108f9f1411997f58e3573634a76ad3180"}}