Differential Privacy

Differential privacy provides one of the strongest mathematical guarantees in data privacy. Its goal is to ensure that whether a single person’s data is included or not does not make a meaningful difference in the published result. This is often achieved by adding carefully controlled noise. It becomes especially important in large-scale statistical reporting, federated systems, and sensitive data sharing scenarios. Differential privacy treats privacy not just as a policy matter, but as a measurable mathematical guarantee.