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.

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