differential-privacy

Differential privacy provides a mathematical framework to guarantee that the inclusion or exclusion of a single individual's data does not significantly affect the outcome of any analysis, thereby protecting personal information. This is achieved through noise addition and algorithms designed to obfuscate data while still allowing for useful aggregate analysis. Misconceptions often arise around its implementation, as it does not eliminate the use of personal data but rather secures it from identification.