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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

Differential privacy using TensorFlow Privacy

TensorFlow Privacy (https://github.com/tensorflow/privacy) is a relatively new addition to the TensorFlow family. This Python library includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. A model that has been trained to be differentially private does not non-trivially change as a result of the removal of any single training instance from its dataset. (Approximate) differential privacy is quantified using epsilon and delta, which give a measure of how sensitive the model is to a change in a single training example. Using the Privacy library is as simple as wrapping the familiar optimizers (for example, RMSprop, Adam, and SGD) to convert ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content