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How secure multi-party computation (sMPC) is relevant to private ML models?Â
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3 points
sMPC can create secure representations of training data in a way that can only be interpreted by a given ML model
sMPC can produce secure representations of training datasets while preserving the correctness of needed attributes
sMPC encrypts training data in a more secure way than traditional models
What is the main use case for Facebook’s CrypTen framework?
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3 points
Enabling the development of new sMPC algorithms for PyTorch models
Incorporating sMPC techniques in the lifecycle of PyTorch programs
Encrypting training datasets using sMPC across different deep learning frameworks
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