Differential Privacy is a technique used to protect individual data while allowing useful insights to be drawn from large datasets. It ensures that the inclusion or exclusion of a single person's data does not significantly affect the outcome of any analysis, thereby safeguarding personal information. This method typically involves adding random noise to the data or the results of queries. By doing so, it becomes difficult to identify specific individuals, even if some information is publicly available. Differential Privacy is widely used in various fields, including healthcare, finance, and technology, to maintain privacy while still enabling data-driven decision-making.