SEALion: a Framework for Neural Network Inference on Encrypted Data

Autor:	van Elsloo, Tim, Patrini, Giorgio, Ivey-Law, Hamish
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. We present two applications that exemplifying the extensibility of our proposal, which are also of independent interest: i) improving efficiency of neural network inference by an activity sparsifier and ii) transfer learning by querying a server-side Variational AutoEncoder that can handle encrypted data.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1904.12840 Zobrazit plný text záznamu View this record from Arxiv