The concept of differential privacy first appeared in the Radar in 2016. Although the problem of breaking privacy through systematic model inference queries was recognized at the time, it was largely a theoretical issue since remedies were few. The industry has lacked tools to prevent this from happening. Opacus is a new Python library that can be used in conjunction with PyTorch to help thwart one type of differential privacy attack. Although this is a promising development, finding the right model and data set to which it applies has been a challenge. The library is still quite new so we're looking forward to seeing how it'll be accepted going forward.