Privacy in Machine Learning Training: DP-SGD
1. Privacy in Machine Learning Training: DP-SGD
Lets talk about privacy in machine learning training. After building machine learning models, it is crucial to carefully consider security measures before exposing them to users, as malicious individuals may attempt various attacks to extract sensitive data or exploit model vulnerabilities. There are many types of attacks and many measures we can take to safeguard against them. While there are many methods, two popular ones for secure models are, differentially private stochastic gradient descent or DP-SGD, and federated learning. DP-SGD is a secure optimization strategy that incorporates differential privacy into stochastic gradient descent or SGD, to train models in a more secure way. As mentioned earlier, differential privacy can be used for many use cases where we aggregate information in a secure way. Machine learning optimization strategies like SGD, which update model parameters by computing gradients of them with respect to a group of data samples, can be viewed as aggregation algorithms. This means differential privacy can be integrated into optimization algorithms like SGD to protect sensitive information during the training process. Although there are multiple implementations, lets take a look at the simplest DP-SGD algorithm here. DP-SGD extends the normal SGD algorithm which computes gradients of each parameter and updates them using the gradients. DP-SGD adds an extra step to introduce differential privacy via gradient clipping and noise edition. The amount of noise can be controlled by the sampling distribution. Larger noise is more secure but can worsen the model performance. With this additional step, we can apply optimization and training in a more secure way. There are many improvements and additional considerations for differential privacy on machine learning training, but you can easily adopt them by using existing implementations. These include TensorFlow Privacy and Google's Differential Privacy Library. TensorFlow Privacy is a library integrated well with the TensorFlow ecosystem. You can easily apply differentially private optimizers including DP-SGD to TensorFlow models using this library. Google's Differential Privacy Library is a more generic library for differential privacy as it is used not only for machine learning purposes. It includes an end-to-end differential privacy framework built on top of Apache Beam, as well as lower level libraries in C++, Go, and Java to implement differentially private aggregations.2. Let's practice!
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