{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:5US2SR7242P4EULB337L2RINXI","short_pith_number":"pith:5US2SR72","canonical_record":{"source":{"id":"1905.04873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T06:16:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2fb313d041f133274aed48fb909ac58c57eadaa737212663ead16f075890cf57","abstract_canon_sha256":"f774d6b756b40dad7597cbec675c345b552d7a735593d77e5231a486846ed563"},"schema_version":"1.0"},"canonical_sha256":"ed25a947fae69fc25161defebd450dba27dac5f974a79ed7b84813eca5142bc3","source":{"kind":"arxiv","id":"1905.04873","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04873","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04873v1","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04873","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"pith_short_12","alias_value":"5US2SR7242P4","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"5US2SR7242P4EULB","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"5US2SR72","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:5US2SR7242P4EULB337L2RINXI","target":"record","payload":{"canonical_record":{"source":{"id":"1905.04873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T06:16:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2fb313d041f133274aed48fb909ac58c57eadaa737212663ead16f075890cf57","abstract_canon_sha256":"f774d6b756b40dad7597cbec675c345b552d7a735593d77e5231a486846ed563"},"schema_version":"1.0"},"canonical_sha256":"ed25a947fae69fc25161defebd450dba27dac5f974a79ed7b84813eca5142bc3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:23.246990Z","signature_b64":"q637Nm/6Ldu6gDObl0hlvl19650ItRWPTIL5V/vmvG+IKN4cFjQV4Jl7S6kclktfAZYOKn84jopuy/EVXYSrBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed25a947fae69fc25161defebd450dba27dac5f974a79ed7b84813eca5142bc3","last_reissued_at":"2026-05-17T23:46:23.246367Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:23.246367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.04873","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":"2026-05-17T23:46:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hW/9sboNv0Hlic4/EdTqzZpInIX6TsDcAYOTPlwPZltfcSVh2/diPBxh7owqqApDFqO4zuVIkolEBDT5qFHbAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:15:11.973054Z"},"content_sha256":"007fd5d6bb7439ea7658bed02f57beeb3e8ec085b7212d3d7b5970a2008c961c","schema_version":"1.0","event_id":"sha256:007fd5d6bb7439ea7658bed02f57beeb3e8ec085b7212d3d7b5970a2008c961c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:5US2SR7242P4EULB337L2RINXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"K S Sesh Kumar, Marc Peter Deisenroth","submitted_at":"2019-05-13T06:16:00Z","abstract_excerpt":"Differential privacy is concerned about the prediction quality while measuring the privacy impact on individuals whose information is contained in the data. We consider differentially private risk minimization problems with regularizers that induce structured sparsity. These regularizers are known to be convex but they are often non-differentiable. We analyze the standard differentially private algorithms, such as output perturbation, Frank-Wolfe and objective perturbation. Output perturbation is a differentially private algorithm that is known to perform well for minimizing risks that are str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04873","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":""},"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":"2026-05-17T23:46:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZDov/Uzx+WF+ORV1viUhgZ0htrCg0o5wQzS4XReUzw2yEQ1g1rVk3lNv9Ga+PFV8ln7ycmfYQfzLn+pX3h8OAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:15:11.973428Z"},"content_sha256":"d272c63ff434a0aaad8099eca8aa1bc10669f092045fea49b220e4db8650a28a","schema_version":"1.0","event_id":"sha256:d272c63ff434a0aaad8099eca8aa1bc10669f092045fea49b220e4db8650a28a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5US2SR7242P4EULB337L2RINXI/bundle.json","state_url":"https://pith.science/pith/5US2SR7242P4EULB337L2RINXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5US2SR7242P4EULB337L2RINXI/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":"2026-05-30T21:15:11Z","links":{"resolver":"https://pith.science/pith/5US2SR7242P4EULB337L2RINXI","bundle":"https://pith.science/pith/5US2SR7242P4EULB337L2RINXI/bundle.json","state":"https://pith.science/pith/5US2SR7242P4EULB337L2RINXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5US2SR7242P4EULB337L2RINXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:5US2SR7242P4EULB337L2RINXI","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":"f774d6b756b40dad7597cbec675c345b552d7a735593d77e5231a486846ed563","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T06:16:00Z","title_canon_sha256":"2fb313d041f133274aed48fb909ac58c57eadaa737212663ead16f075890cf57"},"schema_version":"1.0","source":{"id":"1905.04873","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04873","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04873v1","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04873","created_at":"2026-05-17T23:46:23Z"},{"alias_kind":"pith_short_12","alias_value":"5US2SR7242P4","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"5US2SR7242P4EULB","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"5US2SR72","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:d272c63ff434a0aaad8099eca8aa1bc10669f092045fea49b220e4db8650a28a","target":"graph","created_at":"2026-05-17T23:46:23Z","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"},"paper":{"abstract_excerpt":"Differential privacy is concerned about the prediction quality while measuring the privacy impact on individuals whose information is contained in the data. We consider differentially private risk minimization problems with regularizers that induce structured sparsity. These regularizers are known to be convex but they are often non-differentiable. We analyze the standard differentially private algorithms, such as output perturbation, Frank-Wolfe and objective perturbation. Output perturbation is a differentially private algorithm that is known to perform well for minimizing risks that are str","authors_text":"K S Sesh Kumar, Marc Peter Deisenroth","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T06:16:00Z","title":"Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04873","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":"sha256:007fd5d6bb7439ea7658bed02f57beeb3e8ec085b7212d3d7b5970a2008c961c","target":"record","created_at":"2026-05-17T23:46:23Z","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":"f774d6b756b40dad7597cbec675c345b552d7a735593d77e5231a486846ed563","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T06:16:00Z","title_canon_sha256":"2fb313d041f133274aed48fb909ac58c57eadaa737212663ead16f075890cf57"},"schema_version":"1.0","source":{"id":"1905.04873","kind":"arxiv","version":1}},"canonical_sha256":"ed25a947fae69fc25161defebd450dba27dac5f974a79ed7b84813eca5142bc3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ed25a947fae69fc25161defebd450dba27dac5f974a79ed7b84813eca5142bc3","first_computed_at":"2026-05-17T23:46:23.246367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:23.246367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q637Nm/6Ldu6gDObl0hlvl19650ItRWPTIL5V/vmvG+IKN4cFjQV4Jl7S6kclktfAZYOKn84jopuy/EVXYSrBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:23.246990Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.04873","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:007fd5d6bb7439ea7658bed02f57beeb3e8ec085b7212d3d7b5970a2008c961c","sha256:d272c63ff434a0aaad8099eca8aa1bc10669f092045fea49b220e4db8650a28a"],"state_sha256":"69257217fbfa7a7d47e15e6e1af4c5d841c5199ed7bf9bade5fa964a413f7d59"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ovHfMSN7S8I89bc5ubovvi/GD8nMR6B+sIrN5XR7VHhiEw/09bc/pn9wdwkFwO4FA64Y6hQJiIBsqG3jjJiQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T21:15:11.976258Z","bundle_sha256":"3c0f2cd2aa4192012cb5a9ee376babac7f1b533ce3f58904433cdc90b55e7bd1"}}