Differential Privacy

Differential Privacy introduces carefully calibrated noise into queries and analytics so that the presence or absence of any individual in a dataset cannot be reliably inferred. Applied to identity systems and behavioral analytics, it allows organizations to learn population-level patterns and train fraud models while limiting re-identification risk for specific users.