{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2VVH2IOWSJI4OLUKPMLAHZ7PZK","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":"078487359e26bb758ef4ede8233a6197ef8ffcca78652f1f488b313e0a34c0bc","cross_cats_sorted":["cs.CR","cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-13T00:42:03Z","title_canon_sha256":"39f9bc5b2197b84b76e04ead903d0e8fc0006ec73bf736cd1966efba57c8854e"},"schema_version":"1.0","source":{"id":"1902.04688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.04688","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"arxiv_version","alias_value":"1902.04688v1","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04688","created_at":"2026-05-17T23:54:06Z"},{"alias_kind":"pith_short_12","alias_value":"2VVH2IOWSJI4","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"2VVH2IOWSJI4OLUK","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"2VVH2IOW","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:564c68b6f2e6518040b450db4339d336b40189738896cd6987a366ff5af5874b","target":"graph","created_at":"2026-05-17T23:54:06Z","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":"Data privacy is an important concern in machine learning, and is fundamentally at odds with the task of training useful learning models, which typically require the acquisition of large amounts of private user data. One possible way of fulfilling the machine learning task while preserving user privacy is to train the model on a transformed, noisy version of the data, which does not reveal the data itself directly to the training procedure. In this work, we analyze the privacy-utility trade-off of two such schemes for the problem of linear regression: additive noise, and random projections. In ","authors_text":"Can Karakus, Mehrdad Showkatbakhsh, Suhas Diggavi","cross_cats":["cs.CR","cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-13T00:42:03Z","title":"Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04688","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:dd106b2d031af5214ecf9ba262c35bbbfb6adc081dd3f224941939df1f54d18a","target":"record","created_at":"2026-05-17T23:54:06Z","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":"078487359e26bb758ef4ede8233a6197ef8ffcca78652f1f488b313e0a34c0bc","cross_cats_sorted":["cs.CR","cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-13T00:42:03Z","title_canon_sha256":"39f9bc5b2197b84b76e04ead903d0e8fc0006ec73bf736cd1966efba57c8854e"},"schema_version":"1.0","source":{"id":"1902.04688","kind":"arxiv","version":1}},"canonical_sha256":"d56a7d21d69251c72e8a7b1603e7efca9ccdb597816cfbffbb0acd29ff2744b0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d56a7d21d69251c72e8a7b1603e7efca9ccdb597816cfbffbb0acd29ff2744b0","first_computed_at":"2026-05-17T23:54:06.281308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:06.281308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xH5R3yR3A1zh5Kd/R0ITatZBeLaFxbFjBZWNgNZgMPUz13sDyRMPf/TzA2JJrZ3+8Pj0LwFA4zBD9s/uhTVfDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:06.281757Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.04688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd106b2d031af5214ecf9ba262c35bbbfb6adc081dd3f224941939df1f54d18a","sha256:564c68b6f2e6518040b450db4339d336b40189738896cd6987a366ff5af5874b"],"state_sha256":"a600a7080b8805d5bd9329e33fe8bbd3004f380be7fd3afaf2bd37b3faab492e"}