Differential Privacy provides a robust defence against attacks on anonymised datasets, such as reidentification and reconstruction attacks. Traditional anonymisation techniques often fail to protect against these sophisticated methods, where attackers can identify individuals or reconstruct sensitive information from supposedly anonymised data. Differential Privacy counters these risks by ensuring that the output of any analysis is not significantly altered by the presence or absence of any single individual's data. By adding carefully calibrated noise to the data or the analysis process, Differential Privacy creates a protective layer that makes it extremely difficult for attackers to infer specific individual information, thereby preserving the privacy and integrity of the data.