{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:Z3GWOKDI4HCXOPKY3XZQKOJEOS","short_pith_number":"pith:Z3GWOKDI","canonical_record":{"source":{"id":"2010.03094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T00:33:24Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"0db369fe1f193eda17e6a5566ecabfde38c389750c61e1ff798030033d555cf2","abstract_canon_sha256":"392df6cd1d2f53e43e852cf6de6f0a1d039f162c0e2db75396fd132fa86d58ce"},"schema_version":"1.0"},"canonical_sha256":"cecd672868e1c5773d58ddf305392474882067048dd3d143846b281c91ee72cd","source":{"kind":"arxiv","id":"2010.03094","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.03094","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"arxiv_version","alias_value":"2010.03094v1","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.03094","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_12","alias_value":"Z3GWOKDI4HCX","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_16","alias_value":"Z3GWOKDI4HCXOPKY","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_8","alias_value":"Z3GWOKDI","created_at":"2026-07-05T01:40:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:Z3GWOKDI4HCXOPKY3XZQKOJEOS","target":"record","payload":{"canonical_record":{"source":{"id":"2010.03094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T00:33:24Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"0db369fe1f193eda17e6a5566ecabfde38c389750c61e1ff798030033d555cf2","abstract_canon_sha256":"392df6cd1d2f53e43e852cf6de6f0a1d039f162c0e2db75396fd132fa86d58ce"},"schema_version":"1.0"},"canonical_sha256":"cecd672868e1c5773d58ddf305392474882067048dd3d143846b281c91ee72cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:59.620788Z","signature_b64":"iVMNLphpi5hY+mUXCfvHZcIZHTIYWWGCm7J+oOXp1e7c5OwIyf1FwsmOASAFB9Ng3pO/G29EsBrWae/QJoLcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cecd672868e1c5773d58ddf305392474882067048dd3d143846b281c91ee72cd","last_reissued_at":"2026-07-05T01:40:59.620272Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:59.620272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.03094","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":"2026-07-05T01:40:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aRk/Fx/O0niufn3YrK2yCJjkOXWk70Ww05+N5vi4rMc6IY4NwYDbCAA6plXK/bqUUj3nsDXV+LeSljlkxkqdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T09:14:12.190169Z"},"content_sha256":"1f719cbc86863c69612b5dd5b4943096b3361d6b352d17731678262f65097f2e","schema_version":"1.0","event_id":"sha256:1f719cbc86863c69612b5dd5b4943096b3361d6b352d17731678262f65097f2e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:Z3GWOKDI4HCXOPKY3XZQKOJEOS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Correlated Differential Privacy: Feature Selection in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Huan Huo, Ping Xiong, Tao Zhang, Tianqing Zhu, Wanlei Zhou, Zahir Tari","submitted_at":"2020-10-07T00:33:24Z","abstract_excerpt":"Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, we propose a correlation reduction scheme with differentially private feature selection c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.03094","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/2010.03094/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":"2026-07-05T01:40:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FNsI4uXO4Qk1MzkWJ8dMWZeilbAsjJbepYpsk3nOc4lpKFFpdZt0ToNy9AaqjJ4NBdu+HVtHYl1L/UQjiHPOAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T09:14:12.190726Z"},"content_sha256":"a179ea69f5b879507552ecbc6e14a9429464ff133ce627eceeb13dcb119855d4","schema_version":"1.0","event_id":"sha256:a179ea69f5b879507552ecbc6e14a9429464ff133ce627eceeb13dcb119855d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/bundle.json","state_url":"https://pith.science/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/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":"2026-08-05T09:14:12Z","links":{"resolver":"https://pith.science/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS","bundle":"https://pith.science/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/bundle.json","state":"https://pith.science/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z3GWOKDI4HCXOPKY3XZQKOJEOS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:Z3GWOKDI4HCXOPKY3XZQKOJEOS","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":"392df6cd1d2f53e43e852cf6de6f0a1d039f162c0e2db75396fd132fa86d58ce","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T00:33:24Z","title_canon_sha256":"0db369fe1f193eda17e6a5566ecabfde38c389750c61e1ff798030033d555cf2"},"schema_version":"1.0","source":{"id":"2010.03094","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.03094","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"arxiv_version","alias_value":"2010.03094v1","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.03094","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_12","alias_value":"Z3GWOKDI4HCX","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_16","alias_value":"Z3GWOKDI4HCXOPKY","created_at":"2026-07-05T01:40:59Z"},{"alias_kind":"pith_short_8","alias_value":"Z3GWOKDI","created_at":"2026-07-05T01:40:59Z"}],"graph_snapshots":[{"event_id":"sha256:a179ea69f5b879507552ecbc6e14a9429464ff133ce627eceeb13dcb119855d4","target":"graph","created_at":"2026-07-05T01:40:59Z","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/2010.03094/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, we propose a correlation reduction scheme with differentially private feature selection c","authors_text":"Huan Huo, Ping Xiong, Tao Zhang, Tianqing Zhu, Wanlei Zhou, Zahir Tari","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T00:33:24Z","title":"Correlated Differential Privacy: Feature Selection in Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.03094","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":"sha256:1f719cbc86863c69612b5dd5b4943096b3361d6b352d17731678262f65097f2e","target":"record","created_at":"2026-07-05T01:40:59Z","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":"392df6cd1d2f53e43e852cf6de6f0a1d039f162c0e2db75396fd132fa86d58ce","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-07T00:33:24Z","title_canon_sha256":"0db369fe1f193eda17e6a5566ecabfde38c389750c61e1ff798030033d555cf2"},"schema_version":"1.0","source":{"id":"2010.03094","kind":"arxiv","version":1}},"canonical_sha256":"cecd672868e1c5773d58ddf305392474882067048dd3d143846b281c91ee72cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cecd672868e1c5773d58ddf305392474882067048dd3d143846b281c91ee72cd","first_computed_at":"2026-07-05T01:40:59.620272Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:40:59.620272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iVMNLphpi5hY+mUXCfvHZcIZHTIYWWGCm7J+oOXp1e7c5OwIyf1FwsmOASAFB9Ng3pO/G29EsBrWae/QJoLcBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:40:59.620788Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.03094","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f719cbc86863c69612b5dd5b4943096b3361d6b352d17731678262f65097f2e","sha256:a179ea69f5b879507552ecbc6e14a9429464ff133ce627eceeb13dcb119855d4"],"state_sha256":"781796554b8dcb4013e6f448320ad015ce2f639e94f4780866835531d8cab40b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PG+dHFegBV7dl8vROk+PMD7u7RSKLNRWNjbj4Glv6hjQEIcIj3b1Hn1FLHrpYhXfry5sddbAlj0DnsJAo3N4DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T09:14:12.194305Z","bundle_sha256":"9fa6e9dfc5e60588e25304a67c58b5b4d82aaec1b7f122a5a3a445fa10c2dae8"}}