Differential privacy is built on several technical foundations, including rigorous mathematical definitions, privacy metrics, and noise mechanisms. At its core, it defines a formal framework for quantifying privacy loss and provides a systematic way to control it. This involves concepts like neighbouring datasets, randomised algorithms, and sensitivity. The mathematical rigour in these foundations ensures that privacy guarantees are precise and robust against various data analysis techniques, including those that might involve external auxiliary information.