Data and Model Encrpytion
Once the model is deployed, the real-time unlabeled data that is provided as input to the model can also be tampered with. The trained model is used for inference and provides a label to this data. To protect data against tampering, we need to protect the data at rest and in communication. To protect the data at rest, symmetric encryption can be used to encode it. To transfer the data, SSL/TLS-based secure channels can be established to provide a secure tunnel. This secure tunnel can be used to transfer the symmetric key and the data can be decrypted on the server before it is provided to the trained model.
This is one of the more efficient and foolproof ways to protect data against tampering.
Symmetric encryption ...
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