Noise plays a critical role in differential privacy. It is used to obscure the specific contributions of individual data points, ensuring that the output of a query is not too sensitive to any single entry in the dataset. The key is to add enough noise to mask individual contributions while retaining the overall structure and utility of the data. The type and amount of noise depend on the privacy parameter (epsilon) and the sensitivity of the function being computed. Common noise distributions used in differential privacy include Laplace and Gaussian distributions. Proper calibration of noise is crucial as it directly impacts the effectiveness of privacy protection and the usability of the output data.