In practice, differential privacy works by adding controlled noise to the data or the query results. This noise is calibrated to mask the contribution of individual data points, ensuring that the output does not significantly change due to any single individual's data. For example, when calculating an average value from a dataset, instead of computing the exact average, differential privacy might involve adding a small random value to the result. The key is that this added randomness is carefully calibrated to balance privacy protection with the utility of the data. This method allows for the extraction of meaningful patterns and trends from a dataset without exposing individual data points. The strength of privacy protection is usually controlled by a parameter (often denoted as epsilon), which determines the level of noise added. A smaller epsilon means stronger privacy but potentially less accurate results, while a larger epsilon provides weaker privacy with more precise outcomes. This trade-off is central to the application of differential privacy in various contexts.