What is the significance of differential privacy in data privacy and protection, and how does it enable privacy-preserving data analysis?

Differential privacy is a privacy-enhancing technique that ensures statistical queries on a dataset do not reveal sensitive information about individual data subjects. By adding noise or randomness to query responses, differential privacy protects against privacy breaches and unauthorized disclosure of sensitive data while allowing accurate aggregate analysis and data-driven decision-making. Differential privacy is increasingly important in data-driven applications such as healthcare, finance, and government, where privacy and confidentiality are paramount.