TensorFlow Privacy is a Python library that provides TensorFlow optimizers modified to support differential privacy training. It includes analysis tools for computing privacy guarantees (such as epsilon-delta bounds) and supports efficient per-example gradient clipping for Keras models. The library enables ML practitioners to train models while mathematically bounding the amount of information that can be learned about any individual training example.