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

research-article

EVA Improved: Compiler and Extension Library for CKKS

Authors:

Sangeeta Chowdhary,

Olli SaarikiviAuthors Info & Claims

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

Pages 43 - 55

https://doi.org/10.1145/3474366.3486929

Published: 15 November 2021 Publication History

Abstract

Homomorphic encryption (HE), especially the CKKS scheme, can be extremely challenging to use. The EVA language and compiler (Dathathri et al., PLDI 2020) was an attempt at addressing this challenge. EVA allows a developer to express their encrypted computation in a simple form with a Python-integrated language called PyEVA. It then compiles the program into an executable form by inserting operations such as relinearization and rescaling, applying optimizations, and choosing encryption parameters with the objective of minimizing execution time. Compiled programs can be executed with a parallelizing back-end against a library of HE primitives.

Our work improves upon the EVA toolchain in several ways: changes to the Python front-end make writing PyEVA programs more natural, while a rework of EVA's C++ APIs makes writing new passes easier. We also implement two new optimizations, common subexpression elimination and reduction balancing, which we show allow users to write simpler and more modular PyEVA programs.

We argue that the abstraction EVA provides is insufficient to resolve some common usability challenges. For example, managing vectors of arbitrary size is non-trivial. To resolve these problems, we demonstrate how building a library of commonly used data structures and functions is simple in PyEVA. EVA's automation allows writing very concise code, which gets fused and optimized together with the user program. We create the beginnings of an EVA Extension Library (EXL), that provides vector and matrix classes and a collection of common statistical functions, to demonstrate the power of this approach.

References

[1]

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. Homomorphic Encryption.org, Toronto, Canada.

[2]

David W Archer, José Manuel Calderón Trilla, Jason Dagit, Alex Malozemoff, Yuriy Polyakov, Kurt Rohloff, and Gerard Ryan. 2019. Ramparts: A programmer- friendly system for building homomorphic encryption applications. In Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied Homomorphic Cryptography. 57--68.

Digital Library

[3]

Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021. TenSEAL: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152 (2021).

[4]

Fabian Boemer, Anamaria Costache, Rosario Cammarota, and Casimir Wierzynski. 2019. ngraph-he2: A high-throughput framework for neural network inference on encrypted data. In Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied Homomorphic Cryptography. 45--56.

Digital Library

[5]

Fabian Boemer, Yixing Lao, Rosario Cammarota, and Casimir Wierzynski. 2019. ngraph-he: A graph compiler for deep learning on homomorphically encrypted data. In Proceedings of the 16th ACM Int'l Conf. on Computing Frontiers. 3--13.

Digital Library

[6]

Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. 2014. (Leveled) fully homomorphic encryption without bootstrapping. ACM Transactions on Computation Theory (TOCT) 6, 3 (2014), 1--36.

Digital Library

[7]

Zvika Brakerski and Vinod Vaikuntanathan. 2011. Fully homomorphic encryption from ring-LWE and security for key dependent messages. In Annual cryptology Conf. Springer, 505--524.

Digital Library

[8]

Sergiu Carpov, Paul Dubrulle, and Renaud Sirdey. 2015. Armadillo: A compilation chain for privacy preserving applications. In Proceedings of the 3rd Int'l Workshop on Security in Cloud Computing. 13--19.

Digital Library

[9]

Hao Chen, Ilaria Chillotti, and Yongsoo Song. 2019. Improved bootstrapping for approximate homomorphic encryption. In Annual Int'l Conf. on the Theory and Applications of Cryptographic Techniques. Springer, 34--54.

[10]

Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, and Yongsoo Song. 2018. Bootstrapping for approximate homomorphic encryption. In Annual Int'l Conf. on the Theory and Applications of Cryptographic Techniques. Springer, 360--384.

[11]

Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, and Yongsoo Song. 2018. A full RNS variant of approximate homomorphic encryption. In Int'l Conf. on Selected Areas in Cryptography. Springer, 347--368.

[12]

Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2017. Homo- morphic encryption for arithmetic of approximate numbers. In Int'l Conf. on the Theory and Application of Cryptology and Information Security. Springer, 409--437.

[13]

Eduardo Chielle, Oleg Mazonka, Nektarios Georgios Tsoutsos, and Michail Ma- niatakos. 2018. E3: A framework for compiling C++ programs with encrypted operands. IACR Cryptol. ePrint Arch. 2018 (2018), 1013.

[14]

Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachène. 2020. TFHE: Fast fully homomorphic encryption over the torus. Journal of Cryptology 33, 1 (2020), 34--91.

Digital Library

[15]

Meghan Cowan, Deeksha Dangwal, Armin Alaghi, Caroline Trippel, Vincent T Lee, and Brandon Reagen. 2021. Porcupine: A synthesizing compiler for vectorized homomorphic encryption. arXiv preprint arXiv:2101.07841 (2021).

[16]

Eric Crockett and Chris Peikert. 2016. Λoλ: Functional lattice cryptography. In Proceedings of the 2016 ACM SIGSAC Conf. on Computer and Communications Security. 993--1005.

Digital Library

[17]

Eric Crockett, Chris Peikert, and Chad Sharp. 2018. Alchemy: A language and compiler for homomorphic encryption made easy. In Proceedings of the 2018 ACM SIGSAC Conf. on Computer and Communications Security. 1020--1037.

Digital Library

[18]

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 41st ACM SIGPLAN Conf. on Programming Language Design and Implementation. 546--561.

Digital Library

[19]

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 40th ACM SIGPLAN Conf. on Programming Language Design and Implementation. 142--156.

Digital Library

[20]

Léo Ducas and Daniele Micciancio. 2015. FHEW: Bootstrapping homomorphic encryption in less than a second. In Annual Int'l Conf. on the Theory and Applications of Cryptographic Techniques. Springer, 617--640.

[21]

Junfeng Fan and Frederik Vercauteren. 2012. Somewhat practical fully homomorphic encryption. IACR Cryptol. ePrint Arch. 2012 (2012), 144.

[22]

Craig Gentry. 2009. Fully homomorphic encryption using ideal lattices. In Pro- ceedings of the forty-first annual ACM Symp. on Theory of computing. 169--178.

Digital Library

[23]

Craig Gentry, Amit Sahai, and Brent Waters. 2013. Homomorphic encryption from learning with errors: Conceptually-simpler, asymptotically-faster, attribute-based. In Annual Cryptology Conf. Springer, 75--92.

[24]

Shruthi Gorantala, Rob Springer, Sean Purser-Haskell, William Lam, Royce Wilson, Asra Ali, Eric P Astor, Itai Zukerman, Sam Ruth, Christoph Dibak, et al. 2021. A general purpose transpiler for fully homomorphic encryption. arXiv preprint arXiv:2106.07893 (2021).

[25]

Matthew Green and Matthew Smith. 2016. Developers are not the enemy!: The need for usable security apis. IEEE Security & Privacy 14, 5 (2016), 40--46.

Digital Library

[26]

Shai Halevi and Victor Shoup. 2014. Algorithms in helib. In Annual Cryptology Conf. Springer, 554--571.

[27]

Kyoohyung Han and Dohyeong Ki. 2020. Better bootstrapping for approximate homomorphic encryption. In Cryptographers' Track at the RSA Conf. Springer, 364--390.

[28]

Lattigo 2020. Lattigo v2.1.1. http://github.com/ldsec/lattigo. EPFL-LDS.

[29]

Donald Nguyen, Andrew Lenharth, and Keshav Pingali. 2013. A lightweight infrastructure for graph analytics. In Proceedings of the Twenty-Fourth ACM Symp. on Operating Systems Principles (Farminton, Pennsylvania) (SOSP '13). Association for Computing Machinery, New York, NY, USA, 456--471. https: //doi.org/10.1145/2517349.2522739

Digital Library

[30]

PALISADE 2021. PALISADE Lattice Cryptography Library (release 1.11.2). https: //palisade-crypto.org.

[31]

Nikhil Patnaik, Joseph Hallett, and Awais Rashid. 2019. Usability smells: An analysis of developers' struggle with crypto libraries. In Fifteenth Symp. on Usable Privacy and Security ({SOUPS}2019).

Digital Library

[32]

Ronald L Rivest, Len Adleman, Michael L Dertouzos, et al. 1978. On data banks and privacy homomorphisms. Foundations of secure computation 4, 11 (1978), 169--180.

[33]

SEAL 2020. Microsoft SEAL (release 3.6). https://github.com/Microsoft/SEAL. Microsoft Research, Redmond, WA.

[34]

SHEEP 2019. SHEEP is a homomorphic encryption evaluation platform. https: //github.com/alan-turing-institute/SHEEP.

[35]

Seoul National University. 2020. HEAAN. https://github.com/snucrypto/HEAAN.

[36]

Tim van Elsloo, Giorgio Patrini, and Hamish Ivey-Law. 2019. SEALion: A framework for neural network inference on encrypted data. arXiv preprint arXiv:1904.12840 (2019).

[37]

Alexander Viand. 2021. SoK: Fully homomorphic encryption compilers. In IEEE Symp. on Security and Privacy.

[38]

Alexander Viand and Hossein Shafagh. 2018. Marble: Making fully homomorphic encryption accessible to all. In Proceedings of the 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography. 49--60.

Digital Library

Cited By

Vos JConti MErkin ZBergamaschi FCostache ARohloff K(2024)Oraqle: A Depth-Aware Secure Computation CompilerProceedings of the 12th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3689945.3694808(43-50)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689945.3694808
Nam IVilela JSchulmann HLi N(2024)The Avg-Act Swap and Plaintext Overflow Detection in Fully Homomorphic Operations Over Deep CircuitsProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653277(127-138)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653277
Park SSong WNam SKim HShin JLee J(2023)HEaaN.MLIR: An Optimizing Compiler for Fast Ring-Based Homomorphic EncryptionProceedings of the ACM on Programming Languages10.1145/35912287:PLDI(196-220)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1145/3591228
Show More Cited By

Index Terms

EVA Improved: Compiler and Extension Library for CKKS
1. Security and privacy
  1. Cryptography
    1. Public key (asymmetric) techniques
      1. Public key encryption

Recommendations

ALCHEMY: A Language and Compiler for Homomorphic Encryption Made easY
CCS '18: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

Fully Homomorphic Encryption (FHE) is a cryptographic "holy grail" that allows a worker to perform arbitrary computations on client-encrypted data, without learning anything about the data itself. Since the first plausible construction in 2009, a ...
A simple separate compilation mechanism for block-structured languages

A very simple and efficient technique for the introduction of separate compilation facilities into compilers for block-structured languages is presented. Using this technique, programs may be compiled in parts while the compile-time checking advantages ...
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 ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CCS '21

Sponsor:

SIGSAC

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

November 15, 2021

Virtual Event, Republic of Korea

Acceptance Rates

Overall Acceptance Rate 6 of 17 submissions, 35%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
380
Total Downloads

Downloads (Last 12 months)67
Downloads (Last 6 weeks)9

Reflects downloads up to 04 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vos JConti MErkin ZBergamaschi FCostache ARohloff K(2024)Oraqle: A Depth-Aware Secure Computation CompilerProceedings of the 12th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3689945.3694808(43-50)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689945.3694808
Nam IVilela JSchulmann HLi N(2024)The Avg-Act Swap and Plaintext Overflow Detection in Fully Homomorphic Operations Over Deep CircuitsProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653277(127-138)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653277
Park SSong WNam SKim HShin JLee J(2023)HEaaN.MLIR: An Optimizing Compiler for Fast Ring-Based Homomorphic EncryptionProceedings of the ACM on Programming Languages10.1145/35912287:PLDI(196-220)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1145/3591228
Kim DGuyot C(2023)Optimized Privacy-Preserving CNN Inference With Fully Homomorphic EncryptionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326363118(2175-2187)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3263631
Ma JXu CWills L(2023)PyTFHE: An End-to-End Compilation and Execution Framework for Fully Homomorphic Encryption Applications2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS57527.2023.00012(24-34)Online publication date: Apr-2023
https://doi.org/10.1109/ISPASS57527.2023.00012
Dhiman SMahato GChakraborty S(2023)Homomorphic Encryption Library, Framework, Toolkit and Accelerator: A ReviewSN Computer Science10.1007/s42979-023-02316-95:1Online publication date: 19-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02316-9
Jovanovic NFischer MSteffen SVechev MYin HStavrou ACremers CShi E(2022)Private and Reliable Neural Network InferenceProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3560709(1663-1677)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548606.3560709
Bertrand Van Ouytsel CDam KLegay A(2022)Symbolic analysis meets federated learning to enhance malware identifierProceedings of the 17th International Conference on Availability, Reliability and Security10.1145/3538969.3538996(1-10)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3538969.3538996
Falcetta ARoveri M(2022)Privacy-preserving time series prediction with temporal convolutional neural networks2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892823(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892823

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents