{"paper":{"title":"Triple-Hoisted Baby-Step Giant-Step Linear Transformation over CKKS Homomorphic Encryption and Hardware Accelerator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A triple-hoisted BSGS algorithm for CKKS linear transforms plus FPGA accelerator reduces off-chip memory access 2.9x and computational latency 5.8x versus prior best designs.","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Sajjad Akherati, Xinmiao Zhang","submitted_at":"2026-05-17T01:57:35Z","abstract_excerpt":"Computations can be directly carried out over ciphertexts using homomorphic encryption (HE), which is indispensable for privacy-preserving cloud computing. Linear transformation is widely used in neural networks, including large language models. However, the implementation of linear transformation over HE requires a large number of ciphertext rotations, which incur significant memory and hardware overhead despite existing simplification techniques. This paper proposes a triple-hoisted baby-step giant-step algorithm that decomposes the baby step further to substantially reduce the number of cip"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"For a set of typical parameters, the proposed design reduces the off-chip memory access by 2.9x compared to the best prior design and achieves a 5.8x reduction in computational latency compared with the baseline design.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The reported gains assume that the chosen decomposition parameters and phase partitioning incur no hidden overheads that would appear in full end-to-end encrypted inference workloads; this premise is stated in the abstract when the authors restrict evaluation to 'a set of typical parameters' without showing workload-level measurements.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A triple-hoisted BSGS algorithm for CKKS linear transforms plus FPGA accelerator reduces off-chip memory access 2.9x and computational latency 5.8x versus prior best designs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"f6a3766a43631dd3f9a39348c2c82cff6c97e94019f864c559785a5c8518041e"},"source":{"id":"2605.17222","kind":"arxiv","version":1},"verdict":{"id":"a095b363-50f7-4c2b-99a7-5f82e6de75ea","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-20T00:00:06.000135Z","strongest_claim":"For a set of typical parameters, the proposed design reduces the off-chip memory access by 2.9x compared to the best prior design and achieves a 5.8x reduction in computational latency compared with the baseline design.","one_line_summary":"A triple-hoisted BSGS algorithm for CKKS linear transforms plus FPGA accelerator reduces off-chip memory access 2.9x and computational latency 5.8x versus prior best designs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The reported gains assume that the chosen decomposition parameters and phase partitioning incur no hidden overheads that would appear in full end-to-end encrypted inference workloads; this premise is stated in the abstract when the authors restrict evaluation to 'a set of typical parameters' without showing workload-level measurements.","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17222/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T22:01:57.916836Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.808450Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"c59679a22c37c1eb005131c32c73d826be3f569883ea3c684dfece003224feef"},"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"}