{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KZNPLCJN4ENWI26HAXDIJFJKKK","short_pith_number":"pith:KZNPLCJN","schema_version":"1.0","canonical_sha256":"565af5892de11b646bc705c684952a52aabaebaa497a96c9961b0f652fbe52a2","source":{"kind":"arxiv","id":"2212.01539","version":1},"attestation_state":"computed","paper":{"title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arturs Backurs, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Jiang Bian, Jiyan He, Nenghai Yu, Xuechen Li, Yin Tat Lee","submitted_at":"2022-12-03T05:20:15Z","abstract_excerpt":"Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \\emph{group-wise clipping}. To reduce the compute time overhead of private learning, we show that \\emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization. This results in private learnin"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2212.01539","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-03T05:20:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c6bae02ebcb8c3d77f04343d6721cc5af94f6a56c37615575da8f17ab1ce280f","abstract_canon_sha256":"374763ca4ad80faf3f73c668564a41c2d25125c52fa7c5686efe50fb78245728"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:56.205443Z","signature_b64":"a+ORHYaanK8FFgNEO31ZzR5UfCYc90PJHsD8Px3oY4SN2H9/iA8WwPR8N0Nr6PwKdfHKdDr/fIUpheA7ltJbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"565af5892de11b646bc705c684952a52aabaebaa497a96c9961b0f652fbe52a2","last_reissued_at":"2026-07-05T05:21:56.204937Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:56.204937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arturs Backurs, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Jiang Bian, Jiyan He, Nenghai Yu, Xuechen Li, Yin Tat Lee","submitted_at":"2022-12-03T05:20:15Z","abstract_excerpt":"Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \\emph{group-wise clipping}. To reduce the compute time overhead of private learning, we show that \\emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization. This results in private learnin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01539","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/2212.01539/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2212.01539","created_at":"2026-07-05T05:21:56.204997+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.01539v1","created_at":"2026-07-05T05:21:56.204997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01539","created_at":"2026-07-05T05:21:56.204997+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZNPLCJN4ENW","created_at":"2026-07-05T05:21:56.204997+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZNPLCJN4ENWI26H","created_at":"2026-07-05T05:21:56.204997+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZNPLCJN","created_at":"2026-07-05T05:21:56.204997+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.10237","citing_title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2602.22611","citing_title":"Mitigating Membership Inference in Intermediate Representations with Differentially Private Training","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK","json":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK.json","graph_json":"https://pith.science/api/pith-number/KZNPLCJN4ENWI26HAXDIJFJKKK/graph.json","events_json":"https://pith.science/api/pith-number/KZNPLCJN4ENWI26HAXDIJFJKKK/events.json","paper":"https://pith.science/paper/KZNPLCJN"},"agent_actions":{"view_html":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK","download_json":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK.json","view_paper":"https://pith.science/paper/KZNPLCJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.01539&json=true","fetch_graph":"https://pith.science/api/pith-number/KZNPLCJN4ENWI26HAXDIJFJKKK/graph.json","fetch_events":"https://pith.science/api/pith-number/KZNPLCJN4ENWI26HAXDIJFJKKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK/action/storage_attestation","attest_author":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK/action/author_attestation","sign_citation":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK/action/citation_signature","submit_replication":"https://pith.science/pith/KZNPLCJN4ENWI26HAXDIJFJKKK/action/replication_record"}},"created_at":"2026-07-05T05:21:56.204997+00:00","updated_at":"2026-07-05T05:21:56.204997+00:00"}