Differential privacy is a mathematical framework that bounds how much any individual's data can influence a system's output: adding calibrated noise so aggregat
A mathematical framework that bounds how much any individual's data can influence a system's output, adding calibrated noise so aggregate insights stay accurate while individual records remain provably protected.
Because 'looks anonymous' fails against re-identification attacks, while differential privacy provides quantifiable guarantees that regulators and researchers accept. It is the rigorous standard behind privacy-preserving analytics and synthetic data.