Differential Privacy
A formal, mathematical guarantee that no individual record in a dataset can be detected from a system’s outputs.
Differential privacy adds carefully calibrated random noise to a computation (a query result, or gradient updates during training) so that the output’s probability distribution barely changes whether or not any single individual’s record was included — giving a provable bound, not just a best-effort promise, on how much any one person’s data could be inferred. It comes at a real cost in accuracy, tuned by a privacy budget parameter (ε), which is the core trade-off practitioners have to make.