{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:447M46Z7Z2KTD4ZHIDNFE7NG7W","short_pith_number":"pith:447M46Z7","canonical_record":{"source":{"id":"2103.01294","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T20:31:50Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"cecbce053347534ca1273fa84ee82d43b8c40c9f3ef630ffb8ff6dd492ea834f","abstract_canon_sha256":"1627a9fd3f6c40df31c5e2e0994a0b93ded9067b712ee23964d5800fa8fdc1ba"},"schema_version":"1.0"},"canonical_sha256":"e73ece7b3fce9531f32740da527da6fdbb1bae95c717d146076523abcb6308a0","source":{"kind":"arxiv","id":"2103.01294","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01294","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01294v3","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01294","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_12","alias_value":"447M46Z7Z2KT","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_16","alias_value":"447M46Z7Z2KTD4ZH","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_8","alias_value":"447M46Z7","created_at":"2026-07-05T02:46:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:447M46Z7Z2KTD4ZHIDNFE7NG7W","target":"record","payload":{"canonical_record":{"source":{"id":"2103.01294","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T20:31:50Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"cecbce053347534ca1273fa84ee82d43b8c40c9f3ef630ffb8ff6dd492ea834f","abstract_canon_sha256":"1627a9fd3f6c40df31c5e2e0994a0b93ded9067b712ee23964d5800fa8fdc1ba"},"schema_version":"1.0"},"canonical_sha256":"e73ece7b3fce9531f32740da527da6fdbb1bae95c717d146076523abcb6308a0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:12.491327Z","signature_b64":"A1WwUubvDfZZbcyR38W/3kco6+gqyfzmeU2MKH63lNs8GA4FAr96XuvXuKT3Qhd65sQZDTH61/mpx+mrI6g2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e73ece7b3fce9531f32740da527da6fdbb1bae95c717d146076523abcb6308a0","last_reissued_at":"2026-07-05T02:46:12.490836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:12.490836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.01294","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:46:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zZh1yM6jXaSV8l1AnAIviExNc0fYTkw+MfUwn/GUwlb3Ys7PpmRvyZiMmMeF7FTqCA2H1pDTTMyyeSJJHAvgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T02:05:48.304793Z"},"content_sha256":"a5d8d2d3473f37e6d63cda7cadc610753365ac65931bed23f4a07d362d6b7799","schema_version":"1.0","event_id":"sha256:a5d8d2d3473f37e6d63cda7cadc610753365ac65931bed23f4a07d362d6b7799"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:447M46Z7Z2KTD4ZHIDNFE7NG7W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Wide Network Learning with Differential Privacy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.LG","authors_text":"Huanyu Zhang, Ilya Mironov, Meisam Hejazinia","submitted_at":"2021-03-01T20:31:50Z","abstract_excerpt":"Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training regimes. One particularly challenging class of neural networks are the wide ones, such as those deployed for NLP typeahead prediction or recommender systems. Observing that these models share something in common--an embedding layer that reduces the dimensionality of the input--we focus on developing a general approach towards training these models that takes advantage of the sparsity of the gradients. More abstractly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01294","kind":"arxiv","version":3},"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/2103.01294/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":"2026-07-05T02:46:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZPANJdHfodo46S6UX95PCQh5WysCTJIQ25mJHS76mLyW6776fLrIlLwNXmmfEw9KCrt2l2TYIpyusKHYOTSVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T02:05:48.305165Z"},"content_sha256":"3076f54e07f1c3c398d8243fb55c2f7baf4c674e1f237f74a2c05105cf3c0227","schema_version":"1.0","event_id":"sha256:3076f54e07f1c3c398d8243fb55c2f7baf4c674e1f237f74a2c05105cf3c0227"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/bundle.json","state_url":"https://pith.science/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/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":"2026-07-28T02:05:48Z","links":{"resolver":"https://pith.science/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W","bundle":"https://pith.science/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/bundle.json","state":"https://pith.science/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/447M46Z7Z2KTD4ZHIDNFE7NG7W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:447M46Z7Z2KTD4ZHIDNFE7NG7W","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":"1627a9fd3f6c40df31c5e2e0994a0b93ded9067b712ee23964d5800fa8fdc1ba","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T20:31:50Z","title_canon_sha256":"cecbce053347534ca1273fa84ee82d43b8c40c9f3ef630ffb8ff6dd492ea834f"},"schema_version":"1.0","source":{"id":"2103.01294","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01294","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01294v3","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01294","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_12","alias_value":"447M46Z7Z2KT","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_16","alias_value":"447M46Z7Z2KTD4ZH","created_at":"2026-07-05T02:46:12Z"},{"alias_kind":"pith_short_8","alias_value":"447M46Z7","created_at":"2026-07-05T02:46:12Z"}],"graph_snapshots":[{"event_id":"sha256:3076f54e07f1c3c398d8243fb55c2f7baf4c674e1f237f74a2c05105cf3c0227","target":"graph","created_at":"2026-07-05T02:46:12Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2103.01294/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training regimes. One particularly challenging class of neural networks are the wide ones, such as those deployed for NLP typeahead prediction or recommender systems. Observing that these models share something in common--an embedding layer that reduces the dimensionality of the input--we focus on developing a general approach towards training these models that takes advantage of the sparsity of the gradients. More abstractly","authors_text":"Huanyu Zhang, Ilya Mironov, Meisam Hejazinia","cross_cats":["cs.DS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T20:31:50Z","title":"Wide Network Learning with Differential Privacy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01294","kind":"arxiv","version":3},"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:a5d8d2d3473f37e6d63cda7cadc610753365ac65931bed23f4a07d362d6b7799","target":"record","created_at":"2026-07-05T02:46:12Z","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":"1627a9fd3f6c40df31c5e2e0994a0b93ded9067b712ee23964d5800fa8fdc1ba","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T20:31:50Z","title_canon_sha256":"cecbce053347534ca1273fa84ee82d43b8c40c9f3ef630ffb8ff6dd492ea834f"},"schema_version":"1.0","source":{"id":"2103.01294","kind":"arxiv","version":3}},"canonical_sha256":"e73ece7b3fce9531f32740da527da6fdbb1bae95c717d146076523abcb6308a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e73ece7b3fce9531f32740da527da6fdbb1bae95c717d146076523abcb6308a0","first_computed_at":"2026-07-05T02:46:12.490836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:46:12.490836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A1WwUubvDfZZbcyR38W/3kco6+gqyfzmeU2MKH63lNs8GA4FAr96XuvXuKT3Qhd65sQZDTH61/mpx+mrI6g2BA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:46:12.491327Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.01294","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5d8d2d3473f37e6d63cda7cadc610753365ac65931bed23f4a07d362d6b7799","sha256:3076f54e07f1c3c398d8243fb55c2f7baf4c674e1f237f74a2c05105cf3c0227"],"state_sha256":"0828472bb0d58c263d381b95c376eeb9247c74e4ef2ea8a51189bf91bc46116f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ya6hjgbRDPyp9CdR/x9igg6exymvh6UZeffStWCWAx3fTJiFi9ctA0OEVoDH22q9wofCHNBljzBukSNX/6p2Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-28T02:05:48.307329Z","bundle_sha256":"307e89f8aa4459d5f29403d8c7f0bd81aec4cad1d5107a5aecb4389339c918c6"}}