{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:U5CB2P26C276W7R57CILWXI5B6","short_pith_number":"pith:U5CB2P26","canonical_record":{"source":{"id":"2310.08425","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:48:14Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"dac2f289d5c5a240cab19bfed4da99becf0d79ab33b251b5b354d3cfb4100ca1","abstract_canon_sha256":"a3ed6dac699e2adf0a60f340fe80b72bb4f1820cd5f7a6d985d28dd4d5b41cac"},"schema_version":"1.0"},"canonical_sha256":"a7441d3f5e16bfeb7e3df890bb5d1d0f9f8b68e063d308bc48c67db92ab31593","source":{"kind":"arxiv","id":"2310.08425","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08425","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08425v1","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08425","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_12","alias_value":"U5CB2P26C276","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_16","alias_value":"U5CB2P26C276W7R5","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_8","alias_value":"U5CB2P26","created_at":"2026-07-05T07:00:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:U5CB2P26C276W7R57CILWXI5B6","target":"record","payload":{"canonical_record":{"source":{"id":"2310.08425","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:48:14Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"dac2f289d5c5a240cab19bfed4da99becf0d79ab33b251b5b354d3cfb4100ca1","abstract_canon_sha256":"a3ed6dac699e2adf0a60f340fe80b72bb4f1820cd5f7a6d985d28dd4d5b41cac"},"schema_version":"1.0"},"canonical_sha256":"a7441d3f5e16bfeb7e3df890bb5d1d0f9f8b68e063d308bc48c67db92ab31593","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:15.327511Z","signature_b64":"ov8lV3abbMraX8s3+ONZmYgod6rmC/hevu0nBzxtdI8zPwul3yfgaAnqPdN40l8Wyn5v/BxqvWSC357opfFBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7441d3f5e16bfeb7e3df890bb5d1d0f9f8b68e063d308bc48c67db92ab31593","last_reissued_at":"2026-07-05T07:00:15.327040Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:15.327040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.08425","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":"2026-07-05T07:00:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URrli1LwwlHVhWg85lmccJO0w11zYJsWYgDuNs+Pv0ZKumCA1B2hiLrXzdEq2m4m9or/wsq5X5nbUYmL9rUTDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:13:48.196838Z"},"content_sha256":"25b696092623b235fedf7e795daaba7c63b8df20ad9551c11f7a821043d8bbf1","schema_version":"1.0","event_id":"sha256:25b696092623b235fedf7e795daaba7c63b8df20ad9551c11f7a821043d8bbf1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:U5CB2P26C276W7R57CILWXI5B6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Differentially Private Non-convex Learning for Multi-layer Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cheng-Long Wang, Di Wang, Hanpu Shen, Yiming Ying, Zihang Xiang","submitted_at":"2023-10-12T15:48:14Z","abstract_excerpt":"This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part, we examine cases with no hidden nodes, specifically focusing on Generalized Linear Models (GLMs). We investigate the well-specific model where the random noise possesses a zero mean, and the link function is both bounded and Lipschitz continuous. We propose several algorithms and our analysis demonstrates the feasibility of achieving an excess population risk that remains invariant to the data dimension. We also delv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08425","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/2310.08425/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":"2026-07-05T07:00:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iHkN7SCT2xFcbE0kG4zgvDXjXU+X35vDRlTaJ8QnBhCNklXRMdKv0LQTluWU45aJUtGKYK30QHHJT27LpnOEAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:13:48.197324Z"},"content_sha256":"f97bd34f65094931c529dcfd3d13bdbf2095355c0259b72ce2ce9c3fa5925961","schema_version":"1.0","event_id":"sha256:f97bd34f65094931c529dcfd3d13bdbf2095355c0259b72ce2ce9c3fa5925961"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U5CB2P26C276W7R57CILWXI5B6/bundle.json","state_url":"https://pith.science/pith/U5CB2P26C276W7R57CILWXI5B6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U5CB2P26C276W7R57CILWXI5B6/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":"2026-08-09T18:13:48Z","links":{"resolver":"https://pith.science/pith/U5CB2P26C276W7R57CILWXI5B6","bundle":"https://pith.science/pith/U5CB2P26C276W7R57CILWXI5B6/bundle.json","state":"https://pith.science/pith/U5CB2P26C276W7R57CILWXI5B6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U5CB2P26C276W7R57CILWXI5B6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:U5CB2P26C276W7R57CILWXI5B6","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":"a3ed6dac699e2adf0a60f340fe80b72bb4f1820cd5f7a6d985d28dd4d5b41cac","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:48:14Z","title_canon_sha256":"dac2f289d5c5a240cab19bfed4da99becf0d79ab33b251b5b354d3cfb4100ca1"},"schema_version":"1.0","source":{"id":"2310.08425","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08425","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08425v1","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08425","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_12","alias_value":"U5CB2P26C276","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_16","alias_value":"U5CB2P26C276W7R5","created_at":"2026-07-05T07:00:15Z"},{"alias_kind":"pith_short_8","alias_value":"U5CB2P26","created_at":"2026-07-05T07:00:15Z"}],"graph_snapshots":[{"event_id":"sha256:f97bd34f65094931c529dcfd3d13bdbf2095355c0259b72ce2ce9c3fa5925961","target":"graph","created_at":"2026-07-05T07:00:15Z","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/2310.08425/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part, we examine cases with no hidden nodes, specifically focusing on Generalized Linear Models (GLMs). We investigate the well-specific model where the random noise possesses a zero mean, and the link function is both bounded and Lipschitz continuous. We propose several algorithms and our analysis demonstrates the feasibility of achieving an excess population risk that remains invariant to the data dimension. We also delv","authors_text":"Cheng-Long Wang, Di Wang, Hanpu Shen, Yiming Ying, Zihang Xiang","cross_cats":["cs.CR","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:48:14Z","title":"Differentially Private Non-convex Learning for Multi-layer Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08425","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":"sha256:25b696092623b235fedf7e795daaba7c63b8df20ad9551c11f7a821043d8bbf1","target":"record","created_at":"2026-07-05T07:00:15Z","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":"a3ed6dac699e2adf0a60f340fe80b72bb4f1820cd5f7a6d985d28dd4d5b41cac","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:48:14Z","title_canon_sha256":"dac2f289d5c5a240cab19bfed4da99becf0d79ab33b251b5b354d3cfb4100ca1"},"schema_version":"1.0","source":{"id":"2310.08425","kind":"arxiv","version":1}},"canonical_sha256":"a7441d3f5e16bfeb7e3df890bb5d1d0f9f8b68e063d308bc48c67db92ab31593","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7441d3f5e16bfeb7e3df890bb5d1d0f9f8b68e063d308bc48c67db92ab31593","first_computed_at":"2026-07-05T07:00:15.327040Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:15.327040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ov8lV3abbMraX8s3+ONZmYgod6rmC/hevu0nBzxtdI8zPwul3yfgaAnqPdN40l8Wyn5v/BxqvWSC357opfFBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:15.327511Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08425","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25b696092623b235fedf7e795daaba7c63b8df20ad9551c11f7a821043d8bbf1","sha256:f97bd34f65094931c529dcfd3d13bdbf2095355c0259b72ce2ce9c3fa5925961"],"state_sha256":"f6b158b9c1ea11cfe348bc8e1b8f50aeefefcb0097d267e45cd837dae8d857f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1fAmJe4WQWSiOvQLfTdaBiuol5dLvUZf7mI+ZLtLMRmsbjRndH/vS5RloUBrRobMdnrBaQrjxclVlhZgJjb2AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:13:48.201051Z","bundle_sha256":"82f6d507ccafbe947b421b442f7f19e9bfefde115dd0ad1b891d764c1b341a11"}}