Differential Privacy

Differential privacy is a robust privacy framework designed to protect individual data points within a dataset while still enabling useful aggregate data analysis. It involves adding controlled noise to the data, ensuring that the presence or absence of any single individual’s information does not significantly affect the output of any analysis. This technique helps prevent the identification of individuals in a dataset, even when combined with other data sources. Differential privacy is increasingly used in various sectors, including healthcare, finance, and public data repositories, to enhance data security and confidentiality. By maintaining a balance between privacy and data utility, differential privacy allows organizations to gain insights from data without compromising personal information.