{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GTBTRODG2G5S4UOBUYNBSC6WV5","short_pith_number":"pith:GTBTRODG","canonical_record":{"source":{"id":"2306.11754","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-19T14:35:28Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"cdd99b20ae26d68c9ff9cb971e48a455d3d7280bfe69c2a97456641f3fae8593","abstract_canon_sha256":"1dbd3d8c2ab03b40ed8c369f600283957bd702a232e9e42814a7435259f0f31e"},"schema_version":"1.0"},"canonical_sha256":"34c338b866d1bb2e51c1a61a190bd6af539626cd6e6bc3ed85402ae5cbd7fae5","source":{"kind":"arxiv","id":"2306.11754","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11754","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11754v1","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11754","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_12","alias_value":"GTBTRODG2G5S","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_16","alias_value":"GTBTRODG2G5S4UOB","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_8","alias_value":"GTBTRODG","created_at":"2026-07-05T06:23:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GTBTRODG2G5S4UOBUYNBSC6WV5","target":"record","payload":{"canonical_record":{"source":{"id":"2306.11754","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-19T14:35:28Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"cdd99b20ae26d68c9ff9cb971e48a455d3d7280bfe69c2a97456641f3fae8593","abstract_canon_sha256":"1dbd3d8c2ab03b40ed8c369f600283957bd702a232e9e42814a7435259f0f31e"},"schema_version":"1.0"},"canonical_sha256":"34c338b866d1bb2e51c1a61a190bd6af539626cd6e6bc3ed85402ae5cbd7fae5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:23:05.679213Z","signature_b64":"N8jBPZs0rpXNApRzTA1z7kEyMX+Z+emH1Tbqy+qF8vN5EpJ0VuTONGyGGYNbgvanwH9ekM0jXpO6+VZPn80wBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34c338b866d1bb2e51c1a61a190bd6af539626cd6e6bc3ed85402ae5cbd7fae5","last_reissued_at":"2026-07-05T06:23:05.678837Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:23:05.678837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.11754","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":"2026-07-05T06:23:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"chlvl5Rm+/SbmD6UktxBRVq0olRApjVmSPym4TgxTJN5+e0037KWsdmfQnV699GJaDIaAcUMU5CDrhu9d37zBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:08.672063Z"},"content_sha256":"da3ec8a6a61169d03f2b227839f5e4391dcd6fd0d3ca1a872f14371d1ac99794","schema_version":"1.0","event_id":"sha256:da3ec8a6a61169d03f2b227839f5e4391dcd6fd0d3ca1a872f14371d1ac99794"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GTBTRODG2G5S4UOBUYNBSC6WV5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Kamil Adamczewski, Mijung Park, Yingchen He","submitted_at":"2023-06-19T14:35:28Z","abstract_excerpt":"Scalability is a significant challenge when it comes to applying differential privacy to training deep neural networks. The commonly used DP-SGD algorithm struggles to maintain a high level of privacy protection while achieving high accuracy on even moderately sized models. To tackle this challenge, we take advantage of the fact that neural networks are overparameterized, which allows us to improve neural network training with differential privacy. Specifically, we introduce a new training paradigm that uses \\textit{pre-pruning} and \\textit{gradient-dropping} to reduce the parameter space and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11754","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/2306.11754/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":"2026-07-05T06:23:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6+AC/KOQXRLTtWf5R0Q+BUpn3eUu0QeL2Cxfpf9xCmSpdNaZam1JCDx4rJobRcw395wkGuPXf8mZSIXRx3gtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:08.673000Z"},"content_sha256":"220cf97054cbb38918af9aed24938f90d6a56ebcea314b54e401d3312cf8a175","schema_version":"1.0","event_id":"sha256:220cf97054cbb38918af9aed24938f90d6a56ebcea314b54e401d3312cf8a175"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/bundle.json","state_url":"https://pith.science/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/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":"2026-08-04T07:22:08Z","links":{"resolver":"https://pith.science/pith/GTBTRODG2G5S4UOBUYNBSC6WV5","bundle":"https://pith.science/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/bundle.json","state":"https://pith.science/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GTBTRODG2G5S4UOBUYNBSC6WV5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GTBTRODG2G5S4UOBUYNBSC6WV5","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":"1dbd3d8c2ab03b40ed8c369f600283957bd702a232e9e42814a7435259f0f31e","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-19T14:35:28Z","title_canon_sha256":"cdd99b20ae26d68c9ff9cb971e48a455d3d7280bfe69c2a97456641f3fae8593"},"schema_version":"1.0","source":{"id":"2306.11754","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11754","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11754v1","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11754","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_12","alias_value":"GTBTRODG2G5S","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_16","alias_value":"GTBTRODG2G5S4UOB","created_at":"2026-07-05T06:23:05Z"},{"alias_kind":"pith_short_8","alias_value":"GTBTRODG","created_at":"2026-07-05T06:23:05Z"}],"graph_snapshots":[{"event_id":"sha256:220cf97054cbb38918af9aed24938f90d6a56ebcea314b54e401d3312cf8a175","target":"graph","created_at":"2026-07-05T06:23:05Z","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/2306.11754/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scalability is a significant challenge when it comes to applying differential privacy to training deep neural networks. The commonly used DP-SGD algorithm struggles to maintain a high level of privacy protection while achieving high accuracy on even moderately sized models. To tackle this challenge, we take advantage of the fact that neural networks are overparameterized, which allows us to improve neural network training with differential privacy. Specifically, we introduce a new training paradigm that uses \\textit{pre-pruning} and \\textit{gradient-dropping} to reduce the parameter space and ","authors_text":"Kamil Adamczewski, Mijung Park, Yingchen He","cross_cats":["cs.CR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-19T14:35:28Z","title":"Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11754","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:da3ec8a6a61169d03f2b227839f5e4391dcd6fd0d3ca1a872f14371d1ac99794","target":"record","created_at":"2026-07-05T06:23:05Z","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":"1dbd3d8c2ab03b40ed8c369f600283957bd702a232e9e42814a7435259f0f31e","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-19T14:35:28Z","title_canon_sha256":"cdd99b20ae26d68c9ff9cb971e48a455d3d7280bfe69c2a97456641f3fae8593"},"schema_version":"1.0","source":{"id":"2306.11754","kind":"arxiv","version":1}},"canonical_sha256":"34c338b866d1bb2e51c1a61a190bd6af539626cd6e6bc3ed85402ae5cbd7fae5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34c338b866d1bb2e51c1a61a190bd6af539626cd6e6bc3ed85402ae5cbd7fae5","first_computed_at":"2026-07-05T06:23:05.678837Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:23:05.678837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N8jBPZs0rpXNApRzTA1z7kEyMX+Z+emH1Tbqy+qF8vN5EpJ0VuTONGyGGYNbgvanwH9ekM0jXpO6+VZPn80wBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:23:05.679213Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.11754","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da3ec8a6a61169d03f2b227839f5e4391dcd6fd0d3ca1a872f14371d1ac99794","sha256:220cf97054cbb38918af9aed24938f90d6a56ebcea314b54e401d3312cf8a175"],"state_sha256":"3b55b982d8b953a921a9ec498c247e755ec5d504d7d4844c68605c1f3362d3b4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DK3t2ZwmpvfOcgjwoZnSosVpgiBXiVuUJ4kdoAxcOVww1MYfR+hn2q0IIl4RsgZs4p4P0O8hYS/bloSZZ368CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:22:08.679775Z","bundle_sha256":"916fc84bb3a9d42b7cb6243ee6c7b9d8d787c2eeb0f6928e552c3be5d239b006"}}