“'Differential privacy' describes a promise, made by a data holder, or curator, to a data subject: 'You will not be affected, adversely or otherwise, by allowing your data to be used in any study or analysis, no matter what other studies, data sets, or information sources, are available.'”Cynthia Dwork, Aaron Roth · The Algorithmic Foundations of Differential Privacy · 2014 · The Algorithmic Foundations of Differential Privacy (2014), cap. 1 — The Promise of Differential Privacy
The noise goes on the answer, not the data, and every answer spends from the budget.
The weakness of k-anonymity is that it depends on what else exists in the world. Differential privacy changes the question: not "can the set be re-identified?", but "does the answer to a query change if a single person enters or leaves the set?". If the answer is almost the same with or without you, nobody learns anything about you in particular, whatever else they know. The mechanism: to every aggregate answer (a sum, an average, a count) random noise is added, calibrated to how much a single individual can move the result. The parameter epsilon is the budget: smaller means more protection and less precision, and every query spends some of it — once the budget is spent, you stop answering. For the warehouse it is a technique for output, not storage: raw data stays whole and protected as in lessons 18–19; what is protected is what leaves — public statistics, reports to partners, training sets. Modern censuses use it exactly this way. The cost is real and must be told to users: on small groups, the noise is comparable to the signal.
Why it matters It is the only definition of privacy that does not collapse when another dataset appears tomorrow. The price is precision, and the price must be stated.