Deep Learning Inferences with Hybrid Homomorphic Encryption

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption offers an opportunity to address these issues, but to our best knowledge, there has been no complete implementation of established deep learning approaches using homomorphic encryption. This paper demonstrates a novel approach which preserves privacy, while efficiently applying deep learning inferences. We show efficient designs for implementing many deep learning functions with homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.
  • TL;DR: We made a feature-rich system for deep learning with encrypted inputs, producing encrypted outputs, preserving privacy.
  • Keywords: deep learning, homomorphic encryption, hybrid homomorphic encryption, privacy preserving, representation learning, neural networks

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