{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZCMVRL7IFTC3RKUGSDUFKMEW2X","short_pith_number":"pith:ZCMVRL7I","schema_version":"1.0","canonical_sha256":"c89958afe82cc5b8aa8690e8553096d5ed27ad77790994a222bf73973e487c54","source":{"kind":"arxiv","id":"2311.01598","version":4},"attestation_state":"computed","paper":{"title":"CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR","cs.PF"],"primary_cat":"cs.CR","authors_text":"Austin Ebel, Benedict Reynwar, Brandon Reagen, Negar Neda","submitted_at":"2023-11-02T21:08:56Z","abstract_excerpt":"Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is impractically slow, preventing it from being used in real applications. A major computational bottleneck in HE is the key-switching operation, accounting for approximately 70% of the overall HE execution time and involving a large amount of data for inputs, intermediates, and keys. Prior research has focused on hardware accelerators to improve HE performance, typically featuring large on-chip SRAMs and high off-chip"},"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":"2311.01598","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-02T21:08:56Z","cross_cats_sorted":["cs.AR","cs.PF"],"title_canon_sha256":"edb2d4d7606fa07854f6dc23cffb1acb8cb0076a0c87938829293558baf93f78","abstract_canon_sha256":"a6ece8bc2aa5bc15aad55d8bdf8f1585585a95c88e5d3be70cb61bff15f83a15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:18:32.487183Z","signature_b64":"msDsZ8X2CPDb1fd9g2gk22qcI92/Cf/sgKNaJb3QiJfeATzBUULY0vMDD5sedfSsXz282m8ubgCFnCxGsHwNAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c89958afe82cc5b8aa8690e8553096d5ed27ad77790994a222bf73973e487c54","last_reissued_at":"2026-07-05T08:18:32.486715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:18:32.486715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR","cs.PF"],"primary_cat":"cs.CR","authors_text":"Austin Ebel, Benedict Reynwar, Brandon Reagen, Negar Neda","submitted_at":"2023-11-02T21:08:56Z","abstract_excerpt":"Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is impractically slow, preventing it from being used in real applications. A major computational bottleneck in HE is the key-switching operation, accounting for approximately 70% of the overall HE execution time and involving a large amount of data for inputs, intermediates, and keys. Prior research has focused on hardware accelerators to improve HE performance, typically featuring large on-chip SRAMs and high off-chip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.01598","kind":"arxiv","version":4},"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/2311.01598/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":"2311.01598","created_at":"2026-07-05T08:18:32.486770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.01598v4","created_at":"2026-07-05T08:18:32.486770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.01598","created_at":"2026-07-05T08:18:32.486770+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCMVRL7IFTC3","created_at":"2026-07-05T08:18:32.486770+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCMVRL7IFTC3RKUG","created_at":"2026-07-05T08:18:32.486770+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCMVRL7I","created_at":"2026-07-05T08:18:32.486770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.12710","citing_title":"Travel Time Based Task Mapping for NoC-Based DNN Accelerator","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X","json":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X.json","graph_json":"https://pith.science/api/pith-number/ZCMVRL7IFTC3RKUGSDUFKMEW2X/graph.json","events_json":"https://pith.science/api/pith-number/ZCMVRL7IFTC3RKUGSDUFKMEW2X/events.json","paper":"https://pith.science/paper/ZCMVRL7I"},"agent_actions":{"view_html":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X","download_json":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X.json","view_paper":"https://pith.science/paper/ZCMVRL7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.01598&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCMVRL7IFTC3RKUGSDUFKMEW2X/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCMVRL7IFTC3RKUGSDUFKMEW2X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X/action/storage_attestation","attest_author":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X/action/author_attestation","sign_citation":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X/action/citation_signature","submit_replication":"https://pith.science/pith/ZCMVRL7IFTC3RKUGSDUFKMEW2X/action/replication_record"}},"created_at":"2026-07-05T08:18:32.486770+00:00","updated_at":"2026-07-05T08:18:32.486770+00:00"}