{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HLGF2UN6XEGDB5JIGJI25U7467","short_pith_number":"pith:HLGF2UN6","schema_version":"1.0","canonical_sha256":"3acc5d51beb90c30f5283251aed3fcf7df3a5d8fac3433fc5f60d0c3302d5bb0","source":{"kind":"arxiv","id":"2502.00068","version":1},"attestation_state":"computed","paper":{"title":"Privacy Preserving Charge Location Prediction for Electric Vehicles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Alsharif Abuadbba, Dimity Miller, Raja Jurdak, Robert Marlin","submitted_at":"2025-01-31T03:14:36Z","abstract_excerpt":"By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs' next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only m"},"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":"2502.00068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-01-31T03:14:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0738b00be2c446d2a9e997ca72f1ec5b489a849acf8a589759572992a355233b","abstract_canon_sha256":"75eda2647ec47e2620cd6dd847cc0b6c0f9b03940c76b18f2ba3ebb736570439"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:02.587893Z","signature_b64":"iZXkT3e3WGmrN+PhWQEuM9fgr7OGD/6sMwDxnDDyzIGpK7TflKzip+kDxGroG/v9pJv7QgeQZJSoAoOAsv5mBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3acc5d51beb90c30f5283251aed3fcf7df3a5d8fac3433fc5f60d0c3302d5bb0","last_reissued_at":"2026-07-05T10:08:02.587406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:02.587406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Privacy Preserving Charge Location Prediction for Electric Vehicles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Alsharif Abuadbba, Dimity Miller, Raja Jurdak, Robert Marlin","submitted_at":"2025-01-31T03:14:36Z","abstract_excerpt":"By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs' next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00068","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/2502.00068/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":"2502.00068","created_at":"2026-07-05T10:08:02.587476+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00068v1","created_at":"2026-07-05T10:08:02.587476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00068","created_at":"2026-07-05T10:08:02.587476+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLGF2UN6XEGD","created_at":"2026-07-05T10:08:02.587476+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLGF2UN6XEGDB5JI","created_at":"2026-07-05T10:08:02.587476+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLGF2UN6","created_at":"2026-07-05T10:08:02.587476+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467","json":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467.json","graph_json":"https://pith.science/api/pith-number/HLGF2UN6XEGDB5JIGJI25U7467/graph.json","events_json":"https://pith.science/api/pith-number/HLGF2UN6XEGDB5JIGJI25U7467/events.json","paper":"https://pith.science/paper/HLGF2UN6"},"agent_actions":{"view_html":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467","download_json":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467.json","view_paper":"https://pith.science/paper/HLGF2UN6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00068&json=true","fetch_graph":"https://pith.science/api/pith-number/HLGF2UN6XEGDB5JIGJI25U7467/graph.json","fetch_events":"https://pith.science/api/pith-number/HLGF2UN6XEGDB5JIGJI25U7467/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467/action/storage_attestation","attest_author":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467/action/author_attestation","sign_citation":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467/action/citation_signature","submit_replication":"https://pith.science/pith/HLGF2UN6XEGDB5JIGJI25U7467/action/replication_record"}},"created_at":"2026-07-05T10:08:02.587476+00:00","updated_at":"2026-07-05T10:08:02.587476+00:00"}