The concept of differential privacy emerged from the need to address privacy concerns in data analysis with mathematical rigour. The initial spark came from conversations in the early 2000s with Helen Nissenbaum and others, who highlighted the challenges of analysing sensitive data like census records without compromising individual privacy. The objective was to create a method that could analyse data for the public good, such as for resource allocation or policy decisions while upholding a strict privacy mandate. This led to the exploration of privacy-preserving techniques in data analysis, specifically focusing on how to analyse and extract useful insights from data without revealing sensitive information about individuals. Differential privacy emerged as a solution, balancing the need for data utility with robust privacy guarantees.