Differential Privacy (DP) plays a crucial role in modern data analysis by enabling the use of sensitive data while protecting individual privacy. Its importance is particularly pronounced in fields where data privacy is paramount, such as healthcare and government. Traditional anonymisation techniques are increasingly insufficient against sophisticated re- identification methods, making DP an essential tool. DP allows organisations to derive insights from data without revealing individual data points, thus ensuring compliance with privacy regulations and ethical standards. This is achieved by adding noise to the data or the analysis process, which prevents the identification of individual contributions in the dataset. DP's role is not just in protecting privacy but also in enabling data sharing and collaborative research, as it provides a framework for safely using sensitive data in various applications.