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HEAX: An Architecture for Computing on Encrypted Data

Authors:

M. Sadegh Riazi,

Blake Pelton, and

Wei DaiAuthors Info & Claims

ASPLOS '20: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems

March 2020

Pages 1295 - 1309

https://doi.org/10.1145/3373376.3378523

Published: 13 March 2020 Publication History

Abstract

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvements. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. Our implementation on reconfigurable hardware demonstrates 164-268× performance improvement for a wide range of FHE parameters.

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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
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Index Terms

HEAX: An Architecture for Computing on Encrypted Data
1. Hardware
2. Security and privacy

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cover image ACM Conferences

ASPLOS '20: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems

March 2020

1412 pages

ISBN:9781450371025

DOI:10.1145/3373376

General Chair:
James Larus
EPFL
,
Program Chairs:
Luis Ceze
University of Washington
,
Karin Strauss
Microsoft

Copyright © 2020 ACM.

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Published: 13 March 2020

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Pelton BSapek AEguro KLo DForin AHumphrey MXi JCox DKarandikar Rde Fine Licht JBabin ECaulfield ABurger D(2024)Wavefront Threading Enables Effective High-Level SynthesisProceedings of the ACM on Programming Languages10.1145/36564208:PLDI(1066-1090)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656420
Liu CTang DSong JZhou HYan SYang F(2024)HMNTT: A Highly Efficient MDC-NTT Architecture for Privacy-preserving ApplicationsProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658734(7-12)Online publication date: 12-Jun-2024
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Kong TLi S(2024)Hardware Acceleration and Implementation of Fully Homomorphic Encryption Over the TorusIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2023.333895371:3(1116-1129)Online publication date: Mar-2024
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