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Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures

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arxiv 2407.13055 v2 pith:SMSPV4ZB submitted 2024-07-17 cs.CR cs.PF

Wonseok Choi , Jongmin Kim , Jung Ho Ahn This is my paper
classification cs.CRcs.PF
keywords cheddararchitecturescomparedencryptionfullygpushomomorphicimplementations
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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 tailored to specific GPU architectures. Further, we alleviate the memory bandwidth burden by adjusting common FHE operational sequences and extensively applying kernel fusion. Cheddar delivers performance improvements of 2.18--4.45$\times$ for representative FHE workloads compared to state-of-the-art GPU implementations.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation

    cs.CR 2025-11 conditional novelty 6.0 of 10

    An MPC-ML compiler that modularizes and auto-tunes operator approximations, delivering 1.2–1.8x speedups over an optimized baseline under user-set accuracy bounds.

  2. FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs

    cs.CR 2025-07 conditional novelty 6.0 of 10

    FIDESlib is a fully open-source CKKS GPU library with all primitives including bootstrapping, reporting over 70x bootstrapping speedup over optimized OpenFHE and better performance than Phantom for shared operations.

  3. FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption

    cs.AR 2026-02 reject novelty 5.0 of 10

    A GPU-integrated 16×8 systolic modulo-multiply-accumulate unit with Barrett reduction is claimed to speed CKKS NTT and base conversion by roughly 1.6–2.1x at 2.4% area cost.

  4. Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives

    cs.CR 2025-07 conditional novelty 4.0 of 10

    A structured survey of PPML efficiency optimizations, grouped into protocol, model, and system levels, with comparisons and future directions.

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