Differential privacy is a framework for ensuring the privacy of individuals' data in statistical databases or algorithms. The goal of differential privacy is to enable the analysis of data while simultaneously protecting the privacy of individual data points. This is achieved by adding random noise or perturbations to the data before sharing it with others, in order to prevent the identification of specific individuals in the dataset. Differential privacy has become increasingly important in the age of big data and machine learning, as it allows for the sharing and analysis of sensitive data without compromising individual privacy.