Differential privacy is defined as a property of algorithms used for data analysis. An algorithm provides ε-differential privacy if, for any two datasets differing in only one individual's data, and for every possible outcome, the probability of the outcome occurring with one dataset is at most e^ε times the probability with the other dataset. Here, ε is a small positive number that quantifies privacy loss, with smaller values indicating stronger privacy. When ε is zero the querier learns nothing from the dataset, when it is infinite they would learn every record in the dataset with absolute assurance. Privacy protection is a sliding scale between these two extremes. This definition ensures that the algorithm's output is not significantly influenced by the presence or absence of any single individual's data, providing a strong guarantee of privacy.