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Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52

Published: 15 November 2021 Publication History

Abstract

Modern implementations of homomorphic encryption (HE) rely heavily on polynomial arithmetic over a finite field. This is particularly true of the BGV, BFV, and CKKS HE schemes. Two of the biggest performance bottlenecks in HE primitives and applications are polynomial modular multiplication and the forward and inverse number-theoretic transform (NTT). Here, we introduce Intel® Homomorphic Encryption Acceleration Library (Intel® HEXL), a C++ library which provides optimized implementations of polynomial arithmetic for Intel® processors. Intel HEXL takes advantage of the recent Intel® Advanced Vector Extensions 512 (Intel® AVX512) instruction set to provide state-of-the-art implementations of the NTT and modular multiplication, measuring up to 7.2x single-threaded speedup over a native C++ baseline. Intel HEXL is available open-source at https://github.com/intel/hexl under the Apache 2.0 license and has been adopted by the Microsoft SEAL and PALISADE homomorphic encryption libraries

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    cover image ACM Conferences
    WAHC '21: Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography
    November 2021
    75 pages
    ISBN:9781450386562
    DOI:10.1145/3474366
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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    Published: 15 November 2021

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    Author Tags

    1. homomorphic encryption
    2. number-theoretic transform (ntt)
    3. privacy-preserving machine learning

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    • (2024)SoK: Fully Homomorphic Encryption AcceleratorsACM Computing Surveys10.1145/367695556:12(1-32)Online publication date: 5-Jul-2024
    • (2024)BoostCom: Towards Efficient Universal Fully Homomorphic Encryption by Boosting the Word-wise ComparisonsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676893(121-132)Online publication date: 14-Oct-2024
    • (2024)YuX: Finite Field Multiplication Based Block Ciphers for Efficient FHE EvaluationIEEE Transactions on Information Theory10.1109/TIT.2024.334941470:5(3729-3749)Online publication date: May-2024
    • (2024)cuXCMP: CUDA-Accelerated Private Comparison Based on Homomorphic EncryptionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326767719(3581-3592)Online publication date: 2024
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    • (2024)CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure2024 International Symposium on Secure and Private Execution Environment Design (SEED)10.1109/SEED61283.2024.00022(119-130)Online publication date: 16-May-2024
    • (2024)Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief SurveyIEEE Internet of Things Journal10.1109/JIOT.2024.338287511:14(24569-24580)Online publication date: 15-Jul-2024
    • (2024)Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00071(915-930)Online publication date: 29-Jun-2024
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