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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.

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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
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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

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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

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]

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Published: 15 November 2021

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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

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