Differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its records.

Differential privacy is a property of a randomized algorithm that computes aggregate information about a large data set without revealing detailed information about the individual data. Specifically, an algorithm is $\varepsilon$-differentially private if the probability of any particular outcome of the algorithm is changed by a factor of at most $e^{\varepsilon}$ by including any additional data point. Thus there is a limit to how much information can leak through the algorithm about a user when that user adds their data.