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Machine learning algorithms have achieved remarkable results and are widelyapplied in a variety of domains. These algorithms often rely on sensitive andprivate data such as medical and financial records. Therefore, it is vital todraw further attention regarding privacy threats and corresponding defensivetechniques applied to machine learning models. In this paper, we presentTenSEAL, an open-source library for Privacy-Preserving Machine Learning usingHomomorphic Encryption that can be easily integrated within popular machinelearning frameworks. We benchmark our implementation using MNIST and show thatan encrypted convolutional neural network can be evaluated in less than asecond, using less than half a megabyte of communication.

TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption