{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MVVUGP2V4RD4YOTODMOY6BILJJ","short_pith_number":"pith:MVVUGP2V","canonical_record":{"source":{"id":"2401.10405","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T22:26:31Z","cross_cats_sorted":[],"title_canon_sha256":"4e2053bf5736d35ea80768b88f408b0f8caac848631378476b480dc4bc8f5cd1","abstract_canon_sha256":"c1e83a14d0acda98d5572d7e633689e51784b94bec01561084c8bfc836e903b1"},"schema_version":"1.0"},"canonical_sha256":"656b433f55e447cc3a6e1b1d8f050b4a69ade2bf902f0ddb979e199a4e97174a","source":{"kind":"arxiv","id":"2401.10405","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10405","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10405v1","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10405","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_12","alias_value":"MVVUGP2V4RD4","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_16","alias_value":"MVVUGP2V4RD4YOTO","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_8","alias_value":"MVVUGP2V","created_at":"2026-07-05T07:35:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MVVUGP2V4RD4YOTODMOY6BILJJ","target":"record","payload":{"canonical_record":{"source":{"id":"2401.10405","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T22:26:31Z","cross_cats_sorted":[],"title_canon_sha256":"4e2053bf5736d35ea80768b88f408b0f8caac848631378476b480dc4bc8f5cd1","abstract_canon_sha256":"c1e83a14d0acda98d5572d7e633689e51784b94bec01561084c8bfc836e903b1"},"schema_version":"1.0"},"canonical_sha256":"656b433f55e447cc3a6e1b1d8f050b4a69ade2bf902f0ddb979e199a4e97174a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:25.717260Z","signature_b64":"N/RF2kH/BMO2ibGwZCKCgfj/NsDEyjPDr1FeCRY8eu8S4WIscU1q+B99KgPz9nyzqwVRXlNWB6iPE8UHL3pTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"656b433f55e447cc3a6e1b1d8f050b4a69ade2bf902f0ddb979e199a4e97174a","last_reissued_at":"2026-07-05T07:35:25.716840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:25.716840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.10405","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":"2026-07-05T07:35:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nu/x6ml6hZHFxJgqNWaXU7EPtIuzEBXw/y+XYpn0Q1r5ieojJZCkUmnWs7m6MlEit0G3TE3onfbq0gsKUUDnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T04:54:09.465090Z"},"content_sha256":"76f4170f22839b45f13a352c44be1b6374329bacd49e48359374761148f8b71f","schema_version":"1.0","event_id":"sha256:76f4170f22839b45f13a352c44be1b6374329bacd49e48359374761148f8b71f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MVVUGP2V4RD4YOTODMOY6BILJJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Giulio Zizzo, Janvi Thakkar, Sergio Maffeis","submitted_at":"2024-01-18T22:26:31Z","abstract_excerpt":"Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work addresses privacy and security concerns, they focus on individual defenses, but in practice, models may undergo simultaneous attacks. This study explores the combination of adversarial training and differentially private training to defend against simultaneous attacks. While differentially-private adversarial training, as presented in DP-Adv, outperforms the other state-of-the-art methods in performance, it lacks formal pri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10405","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.10405/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":"2026-07-05T07:35:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p3fxL/tvZzVz04BY3ENE95bBSX02cNPZeWhCEwjB+t/N6sTnnnPOuH/2XAvlZRJIEP2smS9hz+/jTS5BACfuBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T04:54:09.465606Z"},"content_sha256":"762ea9a6d0c03178463040f9c7cccd4a0da9f75e2bbca9b45a0619f392dd96da","schema_version":"1.0","event_id":"sha256:762ea9a6d0c03178463040f9c7cccd4a0da9f75e2bbca9b45a0619f392dd96da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/bundle.json","state_url":"https://pith.science/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/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":"2026-08-19T04:54:09Z","links":{"resolver":"https://pith.science/pith/MVVUGP2V4RD4YOTODMOY6BILJJ","bundle":"https://pith.science/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/bundle.json","state":"https://pith.science/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MVVUGP2V4RD4YOTODMOY6BILJJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MVVUGP2V4RD4YOTODMOY6BILJJ","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":"c1e83a14d0acda98d5572d7e633689e51784b94bec01561084c8bfc836e903b1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T22:26:31Z","title_canon_sha256":"4e2053bf5736d35ea80768b88f408b0f8caac848631378476b480dc4bc8f5cd1"},"schema_version":"1.0","source":{"id":"2401.10405","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10405","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10405v1","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10405","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_12","alias_value":"MVVUGP2V4RD4","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_16","alias_value":"MVVUGP2V4RD4YOTO","created_at":"2026-07-05T07:35:25Z"},{"alias_kind":"pith_short_8","alias_value":"MVVUGP2V","created_at":"2026-07-05T07:35:25Z"}],"graph_snapshots":[{"event_id":"sha256:762ea9a6d0c03178463040f9c7cccd4a0da9f75e2bbca9b45a0619f392dd96da","target":"graph","created_at":"2026-07-05T07:35:25Z","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/2401.10405/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work addresses privacy and security concerns, they focus on individual defenses, but in practice, models may undergo simultaneous attacks. This study explores the combination of adversarial training and differentially private training to defend against simultaneous attacks. While differentially-private adversarial training, as presented in DP-Adv, outperforms the other state-of-the-art methods in performance, it lacks formal pri","authors_text":"Giulio Zizzo, Janvi Thakkar, Sergio Maffeis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T22:26:31Z","title":"Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10405","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":"sha256:76f4170f22839b45f13a352c44be1b6374329bacd49e48359374761148f8b71f","target":"record","created_at":"2026-07-05T07:35:25Z","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":"c1e83a14d0acda98d5572d7e633689e51784b94bec01561084c8bfc836e903b1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T22:26:31Z","title_canon_sha256":"4e2053bf5736d35ea80768b88f408b0f8caac848631378476b480dc4bc8f5cd1"},"schema_version":"1.0","source":{"id":"2401.10405","kind":"arxiv","version":1}},"canonical_sha256":"656b433f55e447cc3a6e1b1d8f050b4a69ade2bf902f0ddb979e199a4e97174a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"656b433f55e447cc3a6e1b1d8f050b4a69ade2bf902f0ddb979e199a4e97174a","first_computed_at":"2026-07-05T07:35:25.716840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:35:25.716840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N/RF2kH/BMO2ibGwZCKCgfj/NsDEyjPDr1FeCRY8eu8S4WIscU1q+B99KgPz9nyzqwVRXlNWB6iPE8UHL3pTDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:35:25.717260Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.10405","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76f4170f22839b45f13a352c44be1b6374329bacd49e48359374761148f8b71f","sha256:762ea9a6d0c03178463040f9c7cccd4a0da9f75e2bbca9b45a0619f392dd96da"],"state_sha256":"a4b1bd960e45b4b8c7478b5a8a8a1ad54c57badf4c66bd6116918fbf87c3982a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N67xHimDsDpnWsxU3eXjPkF2VHhfApOketdsFq8WfQSNk1dFdB6WPvdNnkcFm4TB2C6W3bBmf/Ni+bCyUyAeCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T04:54:09.471269Z","bundle_sha256":"c971670dbb507283d92b9b2c6b8df29a694d27acbb25f038aabf42a3fd97f51e"}}