Corpus ID: 165163848

Partially Encrypted Machine Learning using Functional Encryption

@article{Ryffel2019PartiallyEM,
  title={Partially Encrypted Machine Learning using Functional Encryption},
  author={T. Ryffel and Edouard Dufour Sans and R. Gay and F. Bach and D. Pointcheval},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.10214}
}
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute… Expand
9 Citations
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Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
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Secure Byzantine-Robust Machine Learning
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