{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:A5LUE5GYQQCEKPEX67F6BEY4TV","short_pith_number":"pith:A5LUE5GY","schema_version":"1.0","canonical_sha256":"07574274d88404453c97f7cbe0931c9d74fa738e55e6d0518081783503ceede1","source":{"kind":"arxiv","id":"2009.13839","version":1},"attestation_state":"computed","paper":{"title":"imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CV","authors_text":"Arun Balaji Buduru, Ponnurangam Kumaraguru, Saurabh Gupta","submitted_at":"2020-09-29T08:03:32Z","abstract_excerpt":"Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN - an information maximizing "},"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":"2009.13839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T08:03:32Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"44537e2b099a17507ca0e8037a018dab6805a6c25ab9e14ab14d9ca7dc7a0e03","abstract_canon_sha256":"c8391feef0772b6d32f57bf9b4f4b1fd0c5d8beb26a675a157f6193494818045"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:38:45.581628Z","signature_b64":"qTSHx0vgL30xdUyvlRh60EGvgDus2V7Sa7DGG5eR6zYoCEPQVptjbgmBjcdWsd0ObdL6MlK0NWW3S0h2OOhXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07574274d88404453c97f7cbe0931c9d74fa738e55e6d0518081783503ceede1","last_reissued_at":"2026-07-05T01:38:45.581244Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:38:45.581244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CV","authors_text":"Arun Balaji Buduru, Ponnurangam Kumaraguru, Saurabh Gupta","submitted_at":"2020-09-29T08:03:32Z","abstract_excerpt":"Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN - an information maximizing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.13839","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/2009.13839/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":"2009.13839","created_at":"2026-07-05T01:38:45.581301+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.13839v1","created_at":"2026-07-05T01:38:45.581301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.13839","created_at":"2026-07-05T01:38:45.581301+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5LUE5GYQQCE","created_at":"2026-07-05T01:38:45.581301+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5LUE5GYQQCEKPEX","created_at":"2026-07-05T01:38:45.581301+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5LUE5GY","created_at":"2026-07-05T01:38:45.581301+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/A5LUE5GYQQCEKPEX67F6BEY4TV","json":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV.json","graph_json":"https://pith.science/api/pith-number/A5LUE5GYQQCEKPEX67F6BEY4TV/graph.json","events_json":"https://pith.science/api/pith-number/A5LUE5GYQQCEKPEX67F6BEY4TV/events.json","paper":"https://pith.science/paper/A5LUE5GY"},"agent_actions":{"view_html":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV","download_json":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV.json","view_paper":"https://pith.science/paper/A5LUE5GY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.13839&json=true","fetch_graph":"https://pith.science/api/pith-number/A5LUE5GYQQCEKPEX67F6BEY4TV/graph.json","fetch_events":"https://pith.science/api/pith-number/A5LUE5GYQQCEKPEX67F6BEY4TV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV/action/storage_attestation","attest_author":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV/action/author_attestation","sign_citation":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV/action/citation_signature","submit_replication":"https://pith.science/pith/A5LUE5GYQQCEKPEX67F6BEY4TV/action/replication_record"}},"created_at":"2026-07-05T01:38:45.581301+00:00","updated_at":"2026-07-05T01:38:45.581301+00:00"}