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arXiv:2104.03152 (cs)
[Submitted on 7 Apr 2021 (v1), last revised 28 Apr 2021 (this version, v2)]

Title:TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

Authors:Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, Alaa Eddine Belfedhal
View a PDF of the paper titled TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption, by Ayoub Benaissa and 3 other authors
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Abstract:Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains. These algorithms often rely on sensitive and private data such as medical and financial records. Therefore, it is vital to draw further attention regarding privacy threats and corresponding defensive techniques applied to machine learning models. In this paper, we present TenSEAL, an open-source library for Privacy-Preserving Machine Learning using Homomorphic Encryption that can be easily integrated within popular machine learning frameworks. We benchmark our implementation using MNIST and show that an encrypted convolutional neural network can be evaluated in less than a second, using less than half a megabyte of communication.
Comments: ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021)
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as: arXiv:2104.03152 [cs.CR]
  (or arXiv:2104.03152v2 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2104.03152
arXiv-issued DOI via DataCite

Submission history

From: Bogdan Cebere [view email]
[v1] Wed, 7 Apr 2021 14:32:38 UTC (544 KB)
[v2] Wed, 28 Apr 2021 04:44:18 UTC (543 KB)
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