Description: Laplacian noise is a type of statistical perturbation added to data to protect individuals’ privacy in the context of differential privacy. This approach is based on probability theory and is used to ensure that the inclusion or exclusion of an individual in a dataset does not significantly affect the results of an analysis. By introducing Laplacian noise, the aim is to make the resulting data useful for analysis while preserving the confidentiality of sensitive information. This type of noise is characterized by its distribution, which follows a Laplace function, meaning it is more likely to generate values close to zero and less likely to produce extreme values. This property is crucial for maintaining data integrity while minimizing the risk of re-identifying individuals. In the field of data anonymization, Laplacian noise has become a fundamental tool, especially in applications that require handling personal information, such as in many areas, including healthcare, finance, and online services, where privacy protection is essential.
