{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JFS7WZPZ4MAQP5UUMX2AYXAM7T","short_pith_number":"pith:JFS7WZPZ","schema_version":"1.0","canonical_sha256":"4965fb65f9e30107f69465f40c5c0cfcf5c56bdf6bfb473817db5dcb8a79dbca","source":{"kind":"arxiv","id":"2205.08886","version":1},"attestation_state":"computed","paper":{"title":"GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DB"],"primary_cat":"cs.LG","authors_text":"Hakan Ferhatosmanoglu, Hongkai Wen, Konstantin Klemmer, Teddy Cunningham","submitted_at":"2022-05-18T12:18:01Z","abstract_excerpt":"Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy guarantees. GeoPointGAN's architecture includes a novel point transformation generator that learns to project randomly generated point co-ordinates into meaningful synthetic co-ordinates that capture both microscopic (e.g., junctions, squares)"},"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":"2205.08886","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T12:18:01Z","cross_cats_sorted":["cs.AI","cs.CR","cs.DB"],"title_canon_sha256":"d2f9d10355bdc4b1b9841b7450f04fc09bb06dc897422f14a9153d285f494295","abstract_canon_sha256":"cc65fe78d8286109d224ae216ee448f3a3e4a7ceb32b4a8aae9370b47ec1a8a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:30.644627Z","signature_b64":"eMWVhRzDpXkZxybv5CZCnMeb6Zfn8T+stDj425PoNlJpyVn8vMuPbd4T9O+0qvNZSYCQxMzhrslzKZr3/xFJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4965fb65f9e30107f69465f40c5c0cfcf5c56bdf6bfb473817db5dcb8a79dbca","last_reissued_at":"2026-07-05T04:24:30.644184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:30.644184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DB"],"primary_cat":"cs.LG","authors_text":"Hakan Ferhatosmanoglu, Hongkai Wen, Konstantin Klemmer, Teddy Cunningham","submitted_at":"2022-05-18T12:18:01Z","abstract_excerpt":"Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy guarantees. GeoPointGAN's architecture includes a novel point transformation generator that learns to project randomly generated point co-ordinates into meaningful synthetic co-ordinates that capture both microscopic (e.g., junctions, squares)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08886","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/2205.08886/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":"2205.08886","created_at":"2026-07-05T04:24:30.644262+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.08886v1","created_at":"2026-07-05T04:24:30.644262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08886","created_at":"2026-07-05T04:24:30.644262+00:00"},{"alias_kind":"pith_short_12","alias_value":"JFS7WZPZ4MAQ","created_at":"2026-07-05T04:24:30.644262+00:00"},{"alias_kind":"pith_short_16","alias_value":"JFS7WZPZ4MAQP5UU","created_at":"2026-07-05T04:24:30.644262+00:00"},{"alias_kind":"pith_short_8","alias_value":"JFS7WZPZ","created_at":"2026-07-05T04:24:30.644262+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09312","citing_title":"What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T","json":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T.json","graph_json":"https://pith.science/api/pith-number/JFS7WZPZ4MAQP5UUMX2AYXAM7T/graph.json","events_json":"https://pith.science/api/pith-number/JFS7WZPZ4MAQP5UUMX2AYXAM7T/events.json","paper":"https://pith.science/paper/JFS7WZPZ"},"agent_actions":{"view_html":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T","download_json":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T.json","view_paper":"https://pith.science/paper/JFS7WZPZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.08886&json=true","fetch_graph":"https://pith.science/api/pith-number/JFS7WZPZ4MAQP5UUMX2AYXAM7T/graph.json","fetch_events":"https://pith.science/api/pith-number/JFS7WZPZ4MAQP5UUMX2AYXAM7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T/action/storage_attestation","attest_author":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T/action/author_attestation","sign_citation":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T/action/citation_signature","submit_replication":"https://pith.science/pith/JFS7WZPZ4MAQP5UUMX2AYXAM7T/action/replication_record"}},"created_at":"2026-07-05T04:24:30.644262+00:00","updated_at":"2026-07-05T04:24:30.644262+00:00"}