Differential privacy

Differential privacy is a mathematical technique that adds carefully calibrated noise to data or computations so results reveal useful patterns while making it very hard to learn anything about any single individual.

Differential privacy sits in the Evaluation & safety part of the Agentik {OS} glossary, which defines the words used to build and run AI agent systems.