SEALion: a Framework for Neural Network Inference on Encrypted Data

Understand

We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption.

• It allows one to learn deep neural networks that can be seamlessly utilized for prediction on encrypted data.
• The framework consists of two layers: the first is built upon TensorFlow and SEAL and exposes standard algebra and deep learning primitives; the second implements a Keras-like syntax for training and inference with neural networks.
• Given a required level of security, a user is abstracted from the details of the encoding and the encryption scheme, allowing quick prototyping.