Differential privacy offers robust protection against data re-identification risks, a common challenge in data anonymisation. By ensuring that the output of a data analysis process is not overly influenced by any single individual's data, differential privacy reduces the risk that someone could use the output to infer sensitive information about an individual. This is achieved through the addition of random noise, which obscures the contribution of individual data points, making it significantly harder for attackers to match anonymised data with external datasets to re-identify individuals.