Google Brain Adds Privacy to TensorFlow
- By John K. Waters
Google's deep learning and AI research team, Google Brain, has added yet another module to TensorFlow, the popular open source machine learning platform. Coming hot on the heels of the much anticipated TensorFlow 2.0 alpha release, TensorFlow Privacy is an open source library designed to make it easier for developers to train machine-learning models with privacy.
The new module addresses the growing challenges associated with training machine learning models on privacy-sensitive datasets -- things like personal photos and email. The module uses the theory of differential privacy, a statistical technique developed by cryptographers to maximize the accuracy of queries from a databases while minimizing the privacy impact on individuals whose information is in the database.
The Google Brain team is also billing the new module as an aid to researchers "by advancing the state of the art in machine learning with strong privacy guarantees." And it hopes the module will "develop into a hub of best-of-breed techniques for training machine-learning models with strong privacy guarantees," the group said in a blog post.
The Google Brain team has published an updated technical whitepaper ("A General Approach to Adding Differential Privacy to Iterative Training Procedures") describing its privacy mechanisms in detail. It also offered some examples of how this privacy mechanism might work, along with instructions for using it, in that blog post. The emphasis in this announcement is how easy it is to implement:
"To use TensorFlow Privacy, no expertise in privacy or its underlying mathematics should be required: those using standard TensorFlow mechanisms should not have to change their model architectures, training procedures, or processes. Instead, to train models that protect privacy for their training data, it is often sufficient for you to make some simple code changes and tune the hyperparameters relevant to privacy."
John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at firstname.lastname@example.org.