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Pyfhel: PYthon For Homomorphic Encryption Libraries

Authors:

Alberto Ibarrondo,

Alexander ViandAuthors Info & Claims

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

Pages 11 - 16

https://doi.org/10.1145/3474366.3486923

Published: 15 November 2021 Publication History

Abstract

Fully Homomorphic Encryption (FHE) allows private computation over encrypted data, disclosing neither the inputs, intermediate values nor results. Thanks to recent advances, FHE has become feasible for a wide range of applications, resulting in an explosion of interest in the topic and ground-breaking real-world deployments. Given the increasing presence of FHE beyond the core academic community, there is increasing demand for easier access to FHE for wider audiences. Efficient implementations of FHE schemes are mostly written in high-performance languages like C++, posing a high entry barrier to novice users. We need to bring FHE to the (higher-level) languages and ecosystems non-experts are already familiar with, such as Python, the de-facto standard language of data science and machine learning. We achieve this through wrapping existing FHE implementations in Python, providing one-click installation and convenience in addition to a significantly higher-level API. In contrast to other similar works, Pyfhel goes beyond merely exposing the underlying API, adding a carefully designed abstraction layer that feels at home in Python. In this paper, we present Pyfhel, introduce its design and usage, and highlight how its unique support for accessing low-level features through a high-level API makes it an ideal teaching tool for lectures on FHE.

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Cited By

Liu WYou LShao YShen XHu GShi JGao S(2025)From accuracy to approximation: A survey on approximate homomorphic encryption and its applicationsComputer Science Review10.1016/j.cosrev.2024.10068955(100689)Online publication date: Feb-2025
https://doi.org/10.1016/j.cosrev.2024.100689
Choi SPatel DZad Tootaghaj DCao LAhmed FSharma P(2024)FedNIC: enhancing privacy-preserving federated learning via homomorphic encryption offload on SmartNICFrontiers in Computer Science10.3389/fcomp.2024.14653526Online publication date: 21-Oct-2024
https://doi.org/10.3389/fcomp.2024.1465352
Abdolmaleki MMomot R(2024)Homomorphic Encrypted Revenue ManagementSSRN Electronic Journal10.2139/ssrn.4724820Online publication date: 2024
https://doi.org/10.2139/ssrn.4724820
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Index Terms

Pyfhel: PYthon For Homomorphic Encryption Libraries
1. Security and privacy
  1. Cryptography
    1. Public key (asymmetric) techniques
      1. Public key encryption
  2. Human and societal aspects of security and privacy
    1. Usability in security and privacy

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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 International 4.0 License.

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

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Association for Computing Machinery

New York, NY, United States

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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%

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CCS '25

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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Liu WYou LShao YShen XHu GShi JGao S(2025)From accuracy to approximation: A survey on approximate homomorphic encryption and its applicationsComputer Science Review10.1016/j.cosrev.2024.10068955(100689)Online publication date: Feb-2025
https://doi.org/10.1016/j.cosrev.2024.100689
Choi SPatel DZad Tootaghaj DCao LAhmed FSharma P(2024)FedNIC: enhancing privacy-preserving federated learning via homomorphic encryption offload on SmartNICFrontiers in Computer Science10.3389/fcomp.2024.14653526Online publication date: 21-Oct-2024
https://doi.org/10.3389/fcomp.2024.1465352
Abdolmaleki MMomot R(2024)Homomorphic Encrypted Revenue ManagementSSRN Electronic Journal10.2139/ssrn.4724820Online publication date: 2024
https://doi.org/10.2139/ssrn.4724820
Kishiyama BAlsmadi I(2024)A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic EncryptionInternational Journal of Science, Technology and Society10.11648/j.ijsts.20241202.1112:2(81-87)Online publication date: 20-Mar-2024
https://doi.org/10.11648/j.ijsts.20241202.11
Chielle EMazonka OGamil HManiatakos M(2024)Coupling bit and modular arithmetic for efficient general-purpose fully homomorphic encryptionACM Transactions on Embedded Computing Systems10.1145/366528023:4(1-28)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3665280
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
Akram AKhan FTahir SIqbal AShah SBaz A(2024)Privacy Preserving Inference for Deep Neural Networks: Optimizing Homomorphic Encryption for Efficient and Secure ClassificationIEEE Access10.1109/ACCESS.2024.335714512(15684-15695)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3357145
Goldenberg MMualem LShahar ASnir SAkavia A(2024)Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryptionGenome Research10.1101/gr.279071.12434:9(1324-1333)Online publication date: 5-Sep-2024
https://doi.org/10.1101/gr.279071.124
Tan ZSee-To ELee KDai HWong M(2024)Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilitiesJournal of Network and Computer Applications10.1016/j.jnca.2024.103996231:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103996
Gao YQuan GHomsi SWen WWang L(2024)Secure and efficient general matrix multiplication on cloud using homomorphic encryptionThe Journal of Supercomputing10.1007/s11227-024-06428-880:18(26394-26434)Online publication date: 26-Aug-2024
https://doi.org/10.1007/s11227-024-06428-8
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