Differential privacy is a privacy-preserving technique that adds statistical noise to datasets, ensuring individual data points cannot be reverse-engineered while still allowing meaningful analysis. It is widely used in AI, data analytics, and government data-sharing initiatives to protect sensitive information while maintaining utility.
Frequently asked questions
How does differential privacy work?
It adds carefully calibrated mathematical noise so that no single individual's record measurably changes the result, while overall patterns stay accurate.
Why is differential privacy important for AI?
It lets teams train and evaluate models on sensitive data without exposing any individual, giving a formal privacy guarantee instead of best-effort masking.
What is the difference between differential privacy and anonymization?
Anonymization removes or masks fields and can often be re-identified; differential privacy gives a provable mathematical bound on what any output can reveal about an individual.