Differential privacy is a technique used to ensure that the release of data or the results of data analysis does not compromise the privacy of individuals within the dataset. It works by adding controlled random noise to the data or query results, making it difficult to trace any specific information back to an individual while still allowing meaningful insights to be derived from the data as a whole. The key principle of differential privacy is to provide a mathematical guarantee that the inclusion or exclusion of a single data point does not significantly affect the outcome, thereby protecting the privacy of individuals even when data is shared or analyzed. This approach is widely used in fields like statistics and machine learning, where it is important to balance data utility with privacy protection.