👉 Differential privacy (DP) is a framework designed to protect individual privacy in data analysis by adding controlled noise to the results of queries or computations, ensuring that the presence or absence of any single individual's data does not significantly affect the outcome. This approach allows for accurate aggregate insights while preventing the identification of specific individuals from the dataset. DP achieves this by introducing randomness in a mathematically rigorous way, balancing privacy and utility through parameters like the privacy budget (epsilon), which quantifies the level of privacy protection. By doing so, differential privacy enables organizations to leverage valuable data insights without compromising the confidentiality of personal information.
