Enabling Homomorphically Encrypted Inference for Large DNN Models

@article{LloretTalavera2021EnablingHE,
  title={Enabling Homomorphically Encrypted Inference for Large DNN Models},
  author={Guillermo Lloret-Talavera and Marc Jord{\`a} and Harald Servat and Fabian Boemer and Chetan Chauhan and Shigeki Tomishima and Nilesh N. Shah and Antonio J. Pe{\~n}a},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.16139}
}
The proliferation of machine learning services in the last few years has raised data privacy concerns. Homomorphic encryption (HE) enables inference using encrypted data but it incurs 100x-10,000x memory and runtime overheads. Secure deep neural network (DNN) inference using HE is currently limited by computing and memory resources, with frameworks requiring hundreds of gigabytes of DRAM to evaluate small models. To overcome these limitations, in this paper we explore the feasibility of… Expand

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