Differential privacy is a rigorous mathematical definition of privacy. In the simplest setting, consider an algorithm that analyzes a dataset and computes statistics about it (such as the data’s mean, variance, median, mode, etc.). Such an algorithm is said to be differentially private if by looking at the output, one cannot tell whether any individual’s data was included in the original dataset or not.

Hat-tip Chris Pollette!

Learn more:

Harvard University Privacy Tools Project. (2021). Differential privacy. https://privacytools.seas.harvard.edu/differential-privacy