Differential Privacy in 5 Minutes

Differential privacy guarantees that the output of a computation changes negligibly whether or not any single individual's data is included. It's achieved by adding calibrated random noise (Laplace or Gaussian) proportional to the query sensitivity divided by the privacy budget epsilon. Smaller epsilon means stronger privacy but lower accuracy — choosing epsilon is the core design decision.