Differential privacy is a technique used to protect individual data in a dataset while still allowing for useful analysis. It ensures that the inclusion or exclusion of a single person's data does not significantly affect the outcome of any analysis, making it difficult to identify specific individuals. To achieve this, differential privacy adds random noise to the data or the results of queries. This noise helps to obscure individual information while still providing accurate insights about the overall dataset. As a result, researchers and companies can share data without compromising the privacy of individuals within that data.