Over the years, differential privacy has evolved to include various variations and extensions to address different scenarios and needs. These include local differential privacy, where individuals randomise their data before sharing it; multi-party differential privacy, suitable for distributed datasets; and concentrated differential privacy, which offers tighter control over the accumulation of privacy loss over multiple computations. Each variation adapts the core principles of differential privacy to specific contexts, offering flexibility in its application while maintaining robust privacy guarantees.