{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2LRVJEY3SDWTSGKA5EY5LL7DPU","short_pith_number":"pith:2LRVJEY3","schema_version":"1.0","canonical_sha256":"d2e354931b90ed391940e931d5afe37d14ae28b9eeb8f238dadf9449986dd5ce","source":{"kind":"arxiv","id":"2304.03228","version":1},"attestation_state":"computed","paper":{"title":"FedBot: Enhancing Privacy in Chatbots with Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Addi Ait-Mlouk, Andreas Hellander, Sadi Alawadi, Salman Toor","submitted_at":"2023-04-04T23:13:52Z","abstract_excerpt":"Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, "},"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":"2304.03228","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-04-04T23:13:52Z","cross_cats_sorted":["cs.AI","cs.CR","cs.LG"],"title_canon_sha256":"31fa8fb420537ced67db2119a32b4a76bcc7a77739d64affc3a09db13f6a2e0a","abstract_canon_sha256":"cf15057ca456de9c34994a32e6f706d6209c9e10b8ee613b0ecc7e9aebae9029"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:58:47.074063Z","signature_b64":"cLSoSTvtKh8xw+b8feuz3Tg2jU1Jj/preSob2+2tYaIcp0y+Pdn1/IP5qDy/7w4ej3ruH+FBvwRNi45wnJw7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2e354931b90ed391940e931d5afe37d14ae28b9eeb8f238dadf9449986dd5ce","last_reissued_at":"2026-07-05T05:58:47.073549Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:58:47.073549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FedBot: Enhancing Privacy in Chatbots with Federated Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Addi Ait-Mlouk, Andreas Hellander, Sadi Alawadi, Salman Toor","submitted_at":"2023-04-04T23:13:52Z","abstract_excerpt":"Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.03228","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/2304.03228/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":"2304.03228","created_at":"2026-07-05T05:58:47.073616+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.03228v1","created_at":"2026-07-05T05:58:47.073616+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03228","created_at":"2026-07-05T05:58:47.073616+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LRVJEY3SDWT","created_at":"2026-07-05T05:58:47.073616+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LRVJEY3SDWTSGKA","created_at":"2026-07-05T05:58:47.073616+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LRVJEY3","created_at":"2026-07-05T05:58:47.073616+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01386","citing_title":"GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU","json":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU.json","graph_json":"https://pith.science/api/pith-number/2LRVJEY3SDWTSGKA5EY5LL7DPU/graph.json","events_json":"https://pith.science/api/pith-number/2LRVJEY3SDWTSGKA5EY5LL7DPU/events.json","paper":"https://pith.science/paper/2LRVJEY3"},"agent_actions":{"view_html":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU","download_json":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU.json","view_paper":"https://pith.science/paper/2LRVJEY3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.03228&json=true","fetch_graph":"https://pith.science/api/pith-number/2LRVJEY3SDWTSGKA5EY5LL7DPU/graph.json","fetch_events":"https://pith.science/api/pith-number/2LRVJEY3SDWTSGKA5EY5LL7DPU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU/action/storage_attestation","attest_author":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU/action/author_attestation","sign_citation":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU/action/citation_signature","submit_replication":"https://pith.science/pith/2LRVJEY3SDWTSGKA5EY5LL7DPU/action/replication_record"}},"created_at":"2026-07-05T05:58:47.073616+00:00","updated_at":"2026-07-05T05:58:47.073616+00:00"}