natural-language-processing-18
Privacy Preserving Deep Learning
3260 papers • 126 benchmarks • 313 datasets
The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).
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