Differential Privacy is a mathematical framework for quantifying and guaranteeing privacy in statistical databases, ensuring that the removal or addition of a single database item does not significantly affect the outcome of any analysis. It provides a robust privacy guarantee by adding calibrated noise to data queries, allowing researchers to extract useful information while protecting individual data entries from being identified.