Encrypting training data to securely run on a cloud platform?

tux_tina

I'd like to securely run on cloud platforms*. *like PyTorch on Amazon Web Services , Google Cloud Platform , Microsoft Azure , or Lightning Studios Are there cryptographic methods (e.g., in PyTorch ) to encrypt training data; and then, after I download the model parameters to my local machine, do inference locally and decrypt the data the model outputs? My question is similar to: " How do I encrypt and decrypt my model when I run inference on...

Originally asked by Geremia on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

There are a handful of projects in the PyTorch ecosystem that help with data privacy. PyTorch Pocket Reference pp. 228-9: Table 8-8. Security and privacy projects Project Description AdverTorch Modules for adversarial examples and defending against attacks PySyft Library for model encryption and privacy Opacus Library for training models with differential privacy CrypTen Framework for privacy preserving ML There's also homomorphic encryption with TenSEAL .

Full answer by Geremia: https://ai.stackexchange.com/a/48310 (CC BY-SA 4.0).

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