Differential privacy is a privacy framework introduced by Dwork et al., grounded in rigorous mathematical theory. Initially developed to address privacy concerns during data release in statistical databases, differential privacy ensures that the probability distribution of an algorithm’s output remains nearly unchanged when a single record is added, removed, or modified in the dataset. This mechanism prevents attackers from inferring sensitive information about individual records by observing the output variations. In terms of implementation, the algorithm achieves differential privacy by adding noise that meets the definition of differential privacy to the data being released.