{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FOWJKUWXD2D5HPFS25XBFXWQGX","short_pith_number":"pith:FOWJKUWX","schema_version":"1.0","canonical_sha256":"2bac9552d71e87d3bcb2d76e12ded035dec6dcebc94cd6ef8204dac358db7aad","source":{"kind":"arxiv","id":"2104.01271","version":2},"attestation_state":"computed","paper":{"title":"PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE","eess.AS"],"primary_cat":"cs.SD","authors_text":"Chao-Han Huck Yang, Chin-Hui Lee, Sabato Marco Siniscalchi","submitted_at":"2021-04-02T23:10:57Z","abstract_excerpt":"We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samp"},"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":"2104.01271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2021-04-02T23:10:57Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","eess.AS"],"title_canon_sha256":"f694c0cd4a02878697d7487dac51aff2ae94e740a5e64161866ac75626475afa","abstract_canon_sha256":"71943c41f642bf9c251263dcdc3f9155f9086b858201698f1113f27488c224cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:54.924582Z","signature_b64":"OlSIp5sOlaqBWDfBLLY9gebPhdw8WLEU05/IhF1TXW7ZpnkUx9+1KJPzBV0vArYcRCvOAkBnBS+gRWzpWlaECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bac9552d71e87d3bcb2d76e12ded035dec6dcebc94cd6ef8204dac358db7aad","last_reissued_at":"2026-07-05T03:20:54.924145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:54.924145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE","eess.AS"],"primary_cat":"cs.SD","authors_text":"Chao-Han Huck Yang, Chin-Hui Lee, Sabato Marco Siniscalchi","submitted_at":"2021-04-02T23:10:57Z","abstract_excerpt":"We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01271","kind":"arxiv","version":2},"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/2104.01271/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":"2104.01271","created_at":"2026-07-05T03:20:54.924204+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.01271v2","created_at":"2026-07-05T03:20:54.924204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01271","created_at":"2026-07-05T03:20:54.924204+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOWJKUWXD2D5","created_at":"2026-07-05T03:20:54.924204+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOWJKUWXD2D5HPFS","created_at":"2026-07-05T03:20:54.924204+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOWJKUWX","created_at":"2026-07-05T03:20:54.924204+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/FOWJKUWXD2D5HPFS25XBFXWQGX","json":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX.json","graph_json":"https://pith.science/api/pith-number/FOWJKUWXD2D5HPFS25XBFXWQGX/graph.json","events_json":"https://pith.science/api/pith-number/FOWJKUWXD2D5HPFS25XBFXWQGX/events.json","paper":"https://pith.science/paper/FOWJKUWX"},"agent_actions":{"view_html":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX","download_json":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX.json","view_paper":"https://pith.science/paper/FOWJKUWX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.01271&json=true","fetch_graph":"https://pith.science/api/pith-number/FOWJKUWXD2D5HPFS25XBFXWQGX/graph.json","fetch_events":"https://pith.science/api/pith-number/FOWJKUWXD2D5HPFS25XBFXWQGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX/action/storage_attestation","attest_author":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX/action/author_attestation","sign_citation":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX/action/citation_signature","submit_replication":"https://pith.science/pith/FOWJKUWXD2D5HPFS25XBFXWQGX/action/replication_record"}},"created_at":"2026-07-05T03:20:54.924204+00:00","updated_at":"2026-07-05T03:20:54.924204+00:00"}