Differential privacy is particularly suited for public data releases, such as census data. By applying differential privacy techniques, census bureaus and similar organisations can release aggregate statistical data without compromising the privacy of individuals represented in the data. This is crucial because census data often contains sensitive personal information. Differential privacy allows for the publication of useful data, like demographic trends and population statistics, while ensuring that this information cannot be used to infer details about any specific individual. The U.S. Census Bureau's adoption of differential privacy for the 2020 Census is a notable example, reflecting a growing recognition of the importance of robust privacy-preserving methods in public data dissemination. This approach ensures that while researchers, policymakers, and the public can access valuable data for analysis and decision-making, the risk of privacy breaches for individuals within the dataset is significantly minimised.