More Web Proxy on the site http://driver.im/

research-article

Open access

CraterLake: a hardware accelerator for efficient unbounded computation on encrypted data

Authors:

Nikola Samardzic,

Aleksandar Krastev,

Nathan Manohar,

Nicholas Genise,

Srinivas Devadas,

Karim Eldefrawy,

Daniel SanchezAuthors Info & Claims

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

Pages 173 - 187

https://doi.org/10.1145/3470496.3527393

Published: 11 June 2022 Publication History

Abstract

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000X over unencrypted computation. Recent FHE accelerators have made strides to bridge this performance gap. Unfortunately, prior accelerators only work well for simple programs, but become inefficient for complex programs, which bring additional costs and challenges.

We present CraterLake, the first FHE accelerator that enables FHE programs of unbounded size (i.e., unbounded multiplicative depth). Such computations require very large ciphertexts (tens of MBs each) and different algorithms that prior work does not support well. To tackle this challenge, CraterLake introduces a new hardware architecture that efficiently scales to very large cipher-texts, novel functional units to accelerate key kernels, and new algorithms and compiler techniques to reduce data movement.

We evaluate CraterLake on deep FHE programs, including deep neural networks like ResNet and LSTMs, where prior work takes minutes to hours per inference on a CPU. CraterLake outperforms a CPU by gmean 4,600X and the best prior FHE accelerator by 11.2X under similar area and power budgets. These speeds enable realtime performance on unbounded FHE programs for the first time.

References

[1]

2020. HEAAN software library. https://github.com/snucrypto/HEAAN.

[2]

2020. Lattigo. https://github.com/ldsec/lattigo.

[3]

Ahmad Qaisar Ahmad Al Badawi, Yuriy Polyakov, Khin Mi Mi Aung, Bharadwaj Veeravalli, and Kurt Rohloff. 2021. Implementation and performance evaluation of RNS variants of the BFV homomorphic encryption scheme. IEEE Transactions on Emerging Topics in Computing 9, 2 (2021).

[4]

Martin Albrecht, Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Shai Halevi, Jeffrey Hoffstein, Kim Laine, Kristin Lauter, Satya Lokam, Daniele Micciancio, Dustin Moody, Travis Morrison, Amit Sahai, and Vinod Vaikuntanathan. 2018. Homomorphic Encryption Security Standard. Technical Report. HomomorphicEncryption.org.

[5]

Martin R Albrecht, Benjamin R Curtis, Amit Deo, Alex Davidson, Rachel Player, Eamonn W Postlethwaite, Fernando Virdia, and Thomas Wunderer. 2018. Estimate all the {LWE, NTRU } schemes!. In Proceedings of the International Conference on Security and Cryptography for Networks (SCN).

Digital Library

[6]

Krste Asanovic. 1998. Vector Microprocessors. Ph.D. Dissertation. EECS Department, University of California, Berkeley.

[7]

Jean-Claude Bajard, Julien Eynard, M Anwar Hasan, and Vincent Zucca. 2016. A full RNS variant of FV-like somewhat homomorphic encryption schemes. In Proceedings of the International Conference on Selected Areas in Cryptography (SAC).

[8]

Laszlo A. Belady. 1966. A study of replacement algorithms for a virtual-storage computer. IBM Systems journal 5, 2 (1966).

[9]

Fabrice Benhamouda, Tancrede Lepoint, Claire Mathieu, and Hang Zhou. 2017. Optimization of bootstrapping in circuits. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA).

Digital Library

[10]

Guido Bertoni, Joan Daemen, Michaël Peeters, Gilles Van Assche, Ronny Van Keer, and Benoît Viguier. 2018. KangarooTwelve: Fast hashing based on Keccak. In Proceedings of the 16th International Conference on Applied Cryptography and Network Security (ACNS).

Digital Library

[11]

Jean-Philippe Bossuat, Christian Mouchet, Juan Troncoso-Pastoriza, and Jean-Pierre Hubaux. 2021. Efficient bootstrapping for approximate homomorphic encryption with non-sparse keys. In Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT).

Digital Library

[12]

Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. 2014. (Leveled) fully homomorphic encryption without bootstrapping. ACM Transactions on Computation Theory (TOCT) 6, 3 (2014).

[13]

Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha. 2019. Low latency privacy preserving inference. In Proceedings of the International Conference on Machine Learning (ICML).

[14]

Hao Chen, Ilaria Chillotti, and Yongsoo Song. 2019. Improved bootstrapping for approximate homomorphic encryption. In Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT).

Digital Library

[15]

Hao Chen, Kim Laine, and Peter Rindal. 2017. Fast Private Set Intersection from Homomorphic Encryption. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS).

Digital Library

[16]

Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2017. Homomorphic encryption for arithmetic of approximate numbers. In Proceedings of the International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT).

[17]

Jack Choquette, Wishwesh Gandhi, Olivier Giroux, Nick Stam, and Ronny Krashinsky. 2021. NVIDIA A100 Tensor Core GPU: Performance and innovation. IEEE Micro 41, 2 (2021).

[18]

David Bruce Cousins, John Golusky, Kurt Rohloff, and Daniel Sumorok. 2014. An FPGA co-processor implementation of Homomorphic Encryption. In Proceedings of the IEEE Conference on High Performance Extreme Computing (HPEC).

[19]

David Bruce Cousins, Kurt Rohloff, Chris Peikert, and Rick Schantz. 2012. An update on SIPHER (Scalable Implementation of Primitives for Homomorphic EncRyption) - FPGA implementation using Simulink. In Proceedings of the IEEE Conference on High Performance Extreme Computing (HPEC).

[20]

D. B. Cousins, K. Rohloff, and D. Sumorok. 2017. Designing an FPGA-Accelerated Homomorphic Encryption Co-Processor. IEEE Transactions on Emerging Topics in Computing 5, 2 (2017).

[21]

Sal Dasgupta, Teja Singh, Ashish Jain, Samuel Naffziger, Deepesh John, Chetan Bisht, and Pradeep Jayaraman. 2020. Radeon RX 5700 Series: The AMD 7nm Energy-Efficient High-Performance GPUs. In Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC).

[22]

Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai, Kim Laine, and Madan Musuvathi. 2020. EVA: An encrypted vector arithmetic language and compiler for efficient homomorphic computation. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI).

Digital Library

[23]

Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, and Todd Mytkowicz. 2019. CHET: An optimizing compiler for fully-homomorphic neural-network inferencing. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI).

Digital Library

[24]

Yarkin Doröz, Erding Öztürk, and Berk Sunar. 2015. Accelerating fully homomorphic encryption in hardware. IEEE Trans. Comput. 64, 6 (2015).

[25]

Axel Feldmann, Nikola Samardzic, Aleksandar Krastev, Srini Devadas, Ron Dreslinski, Christopher Peikert, and Daniel Sanchez. 2021. F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption. In Proceedings of the 54th annual IEEE/ACM international symposium on Microarchitecture (MICRO-54).

[26]

Harvey L Garner. 1959. The residue number system. In Papers presented at the the March 3--5, 1959, Western Joint Computer Conference.

[27]

Craig Gentry and Shai Halevi. 2019. Compressible FHE with Applications to PIR. In Proceedings of the Theory of Cryptography Conference (TCC).

Digital Library

[28]

Craig Gentry, Shai Halevi, and Nigel P Smart. 2012. Homomorphic evaluation of the AES circuit. In Proceedings of the 32nd Annual Cryptology Conference (CRYPTO).

Digital Library

[29]

Craig Gentry, Amit Sahai, and Brent Waters. 2013. Homomorphic encryption from learning with errors: Conceptually-simpler, asymptotically-faster, attribute-based. In Proceedings of the 33rd Annual Cryptology Conference (CRYPTO).

[30]

Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy. In Proceedings of the International Conference on Machine Learning (ICML).

[31]

Shai Halevi and Victor Shoup. 2018. Faster homomorphic linear transformations in HElib. In Proceedings of the 38th Annual International Cryptology Conference (CRYPTO).

Digital Library

[32]

Shai Halevi and Victor Shoup. 2020. Design and implementation of HElib: a homomorphic encryption library. Cryptology ePrint Archive, Report 2020/1481.

[33]

Shai Halevi and Victor Shoup. 2020. HElib design principles. Technical Report.

[34]

Shai Halevi and Victor Shoup. 2021. Bootstrapping for HElib. Journal of Cryptology 34, 1 (2021).

Digital Library

[35]

Kyoohyung Han, Seungwan Hong, Jung Hee Cheon, and Daejun Park. 2018. Efficient Logistic Regression on Large Encrypted Data. Cryptology ePrint Archive, Report 2018/662.

[36]

Kyoohyung Han, Seungwan Hong, Jung Hee Cheon, and Daejun Park. 2019. Logistic regression on homomorphic encrypted data at scale. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33.

Digital Library

[37]

Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Adam Hartwig. 2019. Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

[38]

Qijing Huang, Aravind Kalaiah, Minwoo Kang, James Demmel, Grace Dinh, John Wawrzynek, Thomas Norell, and Yakun Sophia Shao. 2021. CoSA: Scheduling by constrained optimization for spatial accelerators. In Proceedings of the 48th annual International Symposium on Computer Architecture (ISCA-48).

Digital Library

[39]

Malika Izabachène, Renaud Sirdey, and Martin Zuber. 2019. Practical fully homomorphic encryption for fully masked neural networks. In Proceedings of the International Conference on Cryptology and Network Security (CANS).

Digital Library

[40]

Jyun-Neng Ji and Ming-Der Shieh. 2019. Efficient comparison and swap on fully homomorphic encrypted data. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS).

[41]

Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al. 2017. In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th annual International Symposium on Computer Architecture (ISCA-44).

Digital Library

[42]

Wonkyung Jung, Sangpyo Kim, Jung Ho Ahn, Jung Hee Cheon, and Younho Lee. 2021. Over 100x faster bootstrapping in fully homomorphic encryption through memory-centric optimization with GPUs. IACR Transactions on Cryptographic Hardware and Embedded Systems (CHES) (2021).

[43]

Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018. GAZELLE: A low latency framework for secure neural network inference. In Proceedings of the 27th USENIX Security Symposium (USENIX Security 18).

[44]

Miran Kim, Yongsoo Song, Baiyu Li, and Daniele Micciancio. 2020. Semi-parallel logistic regression for GWAS on encrypted data. BMC Medical Genomics 13, 7 (2020).

[45]

Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. Technical Report. University of Toronto.

[46]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998).

[47]

Junghyun Lee, Eunsang Lee, Joon-Woo Lee, Yongjune Kim, Young-Sik Kim, and Jong-Seon No. 2021. Precise approximation of convolutional neural networks for homomorphically encrypted data. arXiv preprint arXiv:2105.10879 (2021).

[48]

Joon-Woo Lee, HyungChul Kang, Yongwoo Lee, Woosuk Choi, Jieun Eom, Maxim Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, et al. 2021. Privacy-preserving machine learning with fully homomorphic encryption for deep neural network. arXiv preprint arXiv:2106.07229 (2021).

[49]

Vadim Lyubashevsky, Chris Peikert, and Oded Regev. 2010. On ideal lattices and learning with errors over rings. In Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT).

Digital Library

[50]

Ahmet Can Mert, Erdinç Öztürk, and Erkay Savaş. 2019. Design and implementation of encryption/decryption architectures for BFV homomorphic encryption scheme. IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2019).

[51]

Vincent Migliore, Cédric Seguin, Maria Mendez Real, Vianney Lapotre, Arnaud Tisserand, Caroline Fontaine, Guy Gogniat, and Russell Tessier. 2017. A High-Speed Accelerator for Homomorphic Encryption using the Karatsuba Algorithm. ACM Transactiosn on Embedded Computer Systems (TECS) 16, 5s (2017).

[52]

Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020. Delphi: A cryptographic inference service for neural networks. In Proceedings of the 29th USENIX Security Symposium (USENIX Security 20).

[53]

Christian Mouchet, Jean-Philippe Bossuat, Juan Troncoso-Pastoriza, and J Hubaux. 2020. Lattigo: A multiparty homomorphic encryption library in Go. In 8th Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC).

[54]

NVIDIA. 2021. NVIDIA DGX Station A100 system architecture. https://images.nvidia.com/aem-dam/Solutions/Data-Center/nvidia-dgx-station-a100-system-architecture-white-paper.pdf.

[55]

Angshuman Parashar, Priyanka Raina, Yakun Sophia Shao, Yu-Hsin Chen, Victor A Ying, Anurag Mukkara, Rangharajan Venkatesan, Brucek Khailany, Stephen W Keckler, and Joel Emer. 2019. Timeloop: A systematic approach to DNN accelerator evaluation. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[56]

Michael Pellauer, Yakun Sophia Shao, Jason Clemons, Neal Crago, Kartik Hegde, Rangharajan Venkatesan, Stephen W Keckler, Christopher W Fletcher, and Joel Emer. 2019. Buffets: An efficient and composable storage idiom for explicit decoupled data orchestration. In Proceedings of the 24th international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-XXIV).

Digital Library

[57]

Robert Podschwadt and Daniel Takabi. 2020. Classification of encrypted word embeddings using recurrent neural networks. In Workshop on Privacy in Natural Language Processing (PrivateNLP @ WSDM).

[58]

Rambus Inc. 2020. White paper: HBM2E and GDDR6: Memory Solutions for AI.

[59]

Brandon Reagen, Wooseok Choi, Yeongil Ko, Vincent Lee, Gu-Yeon Wei, Hsien-Hsin S Lee, and David Brooks. 2021. Cheetah: Optimizations and methods for privacy preserving inference via homomorphic encryption. In Proceedings of the 27th IEEE international symposium on High Performance Computer Architecture (HPCA-27).

[60]

M Sadegh Riazi, Kim Laine, Blake Pelton, and Wei Dai. 2020. HEAX: An architecture for computing on encrypted data. In Proceedings of the 25th international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-XXV).

Digital Library

[61]

Sujoy Sinha Roy, Furkan Turan, Kimmo Järvinen, Frederik Vercauteren, and Ingrid Verbauwhede. 2019. FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data. In Proceedings of the 25th IEEE international symposium on High Performance Computer Architecture (HPCA-25).

[62]

Richard M Russell. 1978. The CRAY-1 computer system. Commun. ACM 21, 1 (1978).

Digital Library

[63]

Furkan Turan, Sujoy Roy, and Ingrid Verbauwhede. 2020. HEAWS: An accelerator for homomorphic encryption on the Amazon AWS FPGA. IEEE Trans. Comput. (2020).

[64]

McKenzie van der Hagen and Brandon Lucia. 2022. Client-optimized algorithms and acceleration for encrypted compute offloading. In Proceedings of the 27th international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-XXVII).

Digital Library

[65]

Wei Wang, Zhilu Chen, and Xinming Huang. 2014. Accelerating leveled fully homomorphic encryption using GPU. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS).

[66]

Wei Wang, Yin Hu, Lianmu Chen, Xinming Huang, and Berk Sunar. 2012. Accelerating fully homomorphic encryption using GPU. In Proceedings fo the IEEE conference on High Performance Extreme Computing (HPEC).

[67]

Wei Wang, Yin Hu, Lianmu Chen, Xinming Huang, and Berk Sunar. 2013. Exploring the feasibility of fully homomorphic encryption. IEEE Trans. Comput. 64, 3 (2013).

[68]

Xuan Yang, Mingyu Gao, Qiaoyi Liu, Jeff Setter, Jing Pu, Ankita Nayak, Steven Bell, Kaidi Cao, Heonjae Ha, Priyanka Raina, et al. 2020. Interstellar: Using Halide's scheduling language to analyze DNN accelerators. In Proceedings of the 25th international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-XXV).

Digital Library

[69]

Geoffrey Yeap, S. S. Lin, Y. M. Chen, H. L. Shang, P. W. Wang, H. C. Lin, Y. C. Peng, J. Y. Sheu, M. Wang, X. Chen, B. R. Yang, C. P. Lin, F. C. Yang, Y. K. Leung, D. W. Lin, et al. 2019. 5nm CMOS Production Technology Platform featuring full-fledged EUV, and High Mobility Channel FinFETs with densest 0.021um2 SRAM cells for Mobile SoC and High Performance Computing Applications. In Proceedings of the 2019 IEEE International Electron Devices Meeting (IEDM).

Cited By

Wang ZIkeda M(2024)High-throughput and fully-pipelined ciphertext multiplier for homomorphic encryptionIEICE Electronics Express10.1587/elex.21.2023062821:6(20230628-20230628)Online publication date: 25-Mar-2024
https://doi.org/10.1587/elex.21.20230628
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
Al Badawi AFaizal Bin Yusof M(2024)Private pathological assessment via machine learning and homomorphic encryptionBioData Mining10.1186/s13040-024-00379-917:1Online publication date: 10-Sep-2024
https://doi.org/10.1186/s13040-024-00379-9
Show More Cited By

Index Terms

CraterLake: a hardware accelerator for efficient unbounded computation on encrypted data
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
2. Security and privacy
  1. Cryptography

Recommendations

Chosen-Ciphertext Secure Multi-identity and Multi-attribute Pure FHE
Cryptology and Network Security
Abstract
A multi-identity pure fully homomorphic encryption (MIFHE) enables a server to perform arbitrary computation on the ciphertexts that are encrypted under different identities. In case of multi-attribute pureFHE (MAFHE), the ciphertexts are ...
CCA-Secure Keyed-Fully Homomorphic Encryption
Proceedings, Part I, of the 19th IACR International Conference on Public-Key Cryptography --- PKC 2016 - Volume 9614

To simultaneously achieve CCA security and homomorphic property for encryption, Emura et al. introduced a new cryptographic primitive named keyed-homomorphic encryption, in which homomorphic ciphertext manipulations can only be performed by someone ...
Bounded key-dependent message security
EUROCRYPT'10: Proceedings of the 29th Annual international conference on Theory and Applications of Cryptographic Techniques

We construct the first public-key encryption scheme that is proven secure (in the standard model, under standard assumptions) even when the attacker gets access to encryptions of arbitrary efficient functions of the secret key. Specifically, under ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

June 2022

1097 pages

ISBN:9781450386104

DOI:10.1145/3470496

General Chairs:
Valentina Salapura
Google
,
Mohamed Zahran
New York University
,
Program Chairs:
Fred Chong
The University of Chicago
,
Lingjia Tang
The University of Michigan

Copyright © 2022 Owner/Author.

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

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

IEEE CS TCAA: IEEE CS technical committee on architectural acoustics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2022

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

DARPA
Wistron Corporation

Conference

ISCA '22

Sponsor:

SIGARCH

ISCA '22: The 49th Annual International Symposium on Computer Architecture

June 18 - 22, 2022

New York, New York

Acceptance Rates

ISCA '22 Paper Acceptance Rate 67 of 400 submissions, 17%;

Overall Acceptance Rate 543 of 3,203 submissions, 17%

Upcoming Conference

ISCA '25

Sponsor:
sigarch

The 52nd Annual International Symposium on Computer Architecture

June 21 - 25, 2025

Tokyo , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

75
Total Citations
View Citations
4,097
Total Downloads

Downloads (Last 12 months)1,533
Downloads (Last 6 weeks)205

Reflects downloads up to 16 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang ZIkeda M(2024)High-throughput and fully-pipelined ciphertext multiplier for homomorphic encryptionIEICE Electronics Express10.1587/elex.21.2023062821:6(20230628-20230628)Online publication date: 25-Mar-2024
https://doi.org/10.1587/elex.21.20230628
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
Al Badawi AFaizal Bin Yusof M(2024)Private pathological assessment via machine learning and homomorphic encryptionBioData Mining10.1186/s13040-024-00379-917:1Online publication date: 10-Sep-2024
https://doi.org/10.1186/s13040-024-00379-9
Knabenhans CViand AMerino-Gallardo AHithnawi ABergamaschi FCostache ARohloff K(2024)vFHE: Verifiable Fully Homomorphic EncryptionProceedings of the 12th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3689945.3694806(11-22)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689945.3694806
Li GYe ZChen DDai WMao GHuang KCheung R(2024)ProgramGalois: A Programmable Generator of Radix-4 Discrete Galois Transformation Architecture for Lattice-Based CryptographyACM Transactions on Reconfigurable Technology and Systems10.1145/368943717:4(1-32)Online publication date: 24-Aug-2024
https://dl.acm.org/doi/10.1145/3689437
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
Ju JPark JKim JKang MKim DCheon JAhn JLuo BLiao XXu JKirda ELie D(2024)NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE BootstrappingProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690375(4361-4375)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690375
He JYang KTang GHuang ZLin LWei CYan YWang WLuo BLiao XXu JKirda ELie D(2024)Rhombus: Fast Homomorphic Matrix-Vector Multiplication for Secure Two-Party InferenceProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690281(2490-2504)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690281
Daftardar AReagen BGarg S(2024)SZKP: A Scalable Accelerator Architecture for Zero-Knowledge ProofsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676898(271-283)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3656019.3676898
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
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents