Differential Privacy with TensorFlow
This article is originally published at https://blogs.rstudio.com/tensorflow/Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no single training example influences the parameters of the trained model in a substantial way. This post introduces TensorFlow Privacy, a library built on top of TensorFlow, that can be used to train differentially private deep learning models from R.
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This article is originally published at https://blogs.rstudio.com/tensorflow/
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