{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SMSPV4ZBFQCCCFZSTB7KDA7YFY","short_pith_number":"pith:SMSPV4ZB","schema_version":"1.0","canonical_sha256":"9324faf3212c04211732987ea183f82e3344ecc8ad23a6f63604cfee7d0b0000","source":{"kind":"arxiv","id":"2407.13055","version":2},"attestation_state":"computed","paper":{"title":"Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.CR","authors_text":"Jongmin Kim, Jung Ho Ahn, Wonseok Choi","submitted_at":"2024-07-17T23:49:18Z","abstract_excerpt":"Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory overhead results in significantly slower performance compared to unencrypted processing. To mitigate this overhead, we present Cheddar, a high-performance FHE library for GPUs, achieving substantial speedups over previous GPU implementations. We systematically enable 32-bit FHE execution, leveraging the 32-bit integer datapath within GPUs. We optimize GPU kernels using efficient low-level primitives and algorithms tai"},"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":"2407.13055","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-07-17T23:49:18Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"0b35357f1a7d795d9e8946d6873920f1d34cd1616f4621ae90c95172e0755d5a","abstract_canon_sha256":"6a864e5cf1a2e47d493af2dd1abfe0bc90a0f36e6e82049e6eb983aba5ec4354"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:14.587788Z","signature_b64":"0y+NJ4goBxyZ3BbV8AeLXMli/WXf2WbPf/U8wCxD5Q0WX2IvbqTbFQns6ZeTFDA5EMvC1QusRVxsB3VEjBkxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9324faf3212c04211732987ea183f82e3344ecc8ad23a6f63604cfee7d0b0000","last_reissued_at":"2026-07-05T11:55:14.587362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:14.587362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.CR","authors_text":"Jongmin Kim, Jung Ho Ahn, Wonseok Choi","submitted_at":"2024-07-17T23:49:18Z","abstract_excerpt":"Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory overhead results in significantly slower performance compared to unencrypted processing. To mitigate this overhead, we present Cheddar, a high-performance FHE library for GPUs, achieving substantial speedups over previous GPU implementations. We systematically enable 32-bit FHE execution, leveraging the 32-bit integer datapath within GPUs. We optimize GPU kernels using efficient low-level primitives and algorithms tai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.13055","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/2407.13055/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":"2407.13055","created_at":"2026-07-05T11:55:14.587418+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.13055v2","created_at":"2026-07-05T11:55:14.587418+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.13055","created_at":"2026-07-05T11:55:14.587418+00:00"},{"alias_kind":"pith_short_12","alias_value":"SMSPV4ZBFQCC","created_at":"2026-07-05T11:55:14.587418+00:00"},{"alias_kind":"pith_short_16","alias_value":"SMSPV4ZBFQCCCFZS","created_at":"2026-07-05T11:55:14.587418+00:00"},{"alias_kind":"pith_short_8","alias_value":"SMSPV4ZB","created_at":"2026-07-05T11:55:14.587418+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.00169","citing_title":"Beyond Latency: A System-Level Characterization of MPC and FHE for PPML","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11659","citing_title":"GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY","json":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY.json","graph_json":"https://pith.science/api/pith-number/SMSPV4ZBFQCCCFZSTB7KDA7YFY/graph.json","events_json":"https://pith.science/api/pith-number/SMSPV4ZBFQCCCFZSTB7KDA7YFY/events.json","paper":"https://pith.science/paper/SMSPV4ZB"},"agent_actions":{"view_html":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY","download_json":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY.json","view_paper":"https://pith.science/paper/SMSPV4ZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.13055&json=true","fetch_graph":"https://pith.science/api/pith-number/SMSPV4ZBFQCCCFZSTB7KDA7YFY/graph.json","fetch_events":"https://pith.science/api/pith-number/SMSPV4ZBFQCCCFZSTB7KDA7YFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY/action/storage_attestation","attest_author":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY/action/author_attestation","sign_citation":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY/action/citation_signature","submit_replication":"https://pith.science/pith/SMSPV4ZBFQCCCFZSTB7KDA7YFY/action/replication_record"}},"created_at":"2026-07-05T11:55:14.587418+00:00","updated_at":"2026-07-05T11:55:14.587418+00:00"}