Mathematics plays a crucial role in studying privacy because it provides a structured and quantifiable way to address privacy concerns in data handling. Historically, many attempts at data anonymisation have failed, revealing individuals' identities unintentionally. This underscores the need for a mathematically rigorous approach to privacy. Differential privacy uses mathematical concepts to formulate privacy guarantees and to construct mechanisms that maintain privacy during data analysis. This formal approach allows for the precise balancing of privacy risks against the utility of the data, ensuring robust protection for individual privacy while allowing valuable insights to be drawn from large datasets.