Differential privacy ensures that the presence or absence of any single individual's data in a dataset does not significantly alter the output of data analysis. This is achieved by adding controlled randomness to the data processing, which creates a protective noise. The key concept is that results from a dataset should be almost indistinguishable whether an individual's data is included or not. This approach balances the need for useful data analysis with the protection of individual privacy, making it particularly effective in fields where data sensitivity is high. It's a mathematically rigorous method that quantifies and controls the privacy loss during data processing.