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Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52

Authors:

Fillipe D.M. de Souza,

Vinodh GopalAuthors Info & Claims

WAHC '21: Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography

Pages 57 - 62

https://doi.org/10.1145/3474366.3486926

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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Cited By

Gong YChang XMišić JMišić VWang JZhu H(2024)Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methodsCybersecurity10.1186/s42400-023-00187-47:1Online publication date: 1-Mar-2024
https://doi.org/10.1186/s42400-023-00187-4
de Souza Fde Lassus HCammarota R(2024)Private detection of relatives in forensic genomics using homomorphic encryptionBMC Medical Genomics10.1186/s12920-024-02037-917:1Online publication date: 19-Nov-2024
https://doi.org/10.1186/s12920-024-02037-9
Zhang JCheng XYang LHu JLiu XChen K(2024)SoK: Fully Homomorphic Encryption AcceleratorsACM Computing Surveys10.1145/367695556:12(1-32)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3676955
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Index Terms

Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52
1. Mathematics of computing
  1. Mathematical software
    1. Mathematical software performance

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Information & Contributors

Information

Published In

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

Program Chairs:
Michael Brenner
Leibniz Universität Hannover, Germany
,
Rachel Player
Royal Holloway, University of London, UK
,
Kurt Rohloff
NJIT and Duality Technologies, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2021

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CCS '21

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SIGSAC

CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security

November 15, 2021

Virtual Event, Republic of Korea

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Overall Acceptance Rate 6 of 17 submissions, 35%

Upcoming Conference

CCS '25

Sponsor:
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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Gong YChang XMišić JMišić VWang JZhu H(2024)Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methodsCybersecurity10.1186/s42400-023-00187-47:1Online publication date: 1-Mar-2024
https://doi.org/10.1186/s42400-023-00187-4
de Souza Fde Lassus HCammarota R(2024)Private detection of relatives in forensic genomics using homomorphic encryptionBMC Medical Genomics10.1186/s12920-024-02037-917:1Online publication date: 19-Nov-2024
https://doi.org/10.1186/s12920-024-02037-9
Zhang JCheng XYang LHu JLiu XChen K(2024)SoK: Fully Homomorphic Encryption AcceleratorsACM Computing Surveys10.1145/367695556:12(1-32)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3676955
Yudha AXue JLou QZhou HSolihin Y(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
https://dl.acm.org/doi/10.1145/3656019.3676893
Liu FLi YChen HJiao LLuo MWang M(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
https://doi.org/10.1109/TIT.2024.3349414
Yang HShen SLiu ZZhao Y(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
https://doi.org/10.1109/TIFS.2023.3267677
Kim ADeryabin MEom JChoi RLee YGhang WYoo D(2024)General Bootstrapping Approach for RLWE-Based Homomorphic EncryptionIEEE Transactions on Computers10.1109/TC.2023.331840573:1(86-96)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TC.2023.3318405
Kim SKim JChoi JAhn J(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
https://doi.org/10.1109/SEED61283.2024.00022
Xie QJiang SJiang LHuang YZhao ZKhan SDai WLiu ZWu K(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
https://doi.org/10.1109/JIOT.2024.3382875
Pan XAn YLiang SMao BZhang MLi QJung MZhang J(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
https://doi.org/10.1109/ISCA59077.2024.00071
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