With a differentially private algorithm, there’s no need to
analyze a question carefully to determine whether it seeks to
invade an individual’s privacy; that protection is automatically
built into the algorithm’s functioning. Because prying questions
usually boil down to small numbers related to specific people
and non-prying questions examine aggregate-level behavior of
large groups, the same amount of added noise that renders
answers about individuals meaningless will have only a minor
effect on answers to many legitimate research questions. With
differential privacy, the kinds of issues that plagued other
data releases — such as attackers cross-referencing data with
outside information — disappear. The approach’s mathematical
privacy guarantees do not depend on the attacker having limited
outside information or resources. “Differential privacy assumes
that the adversary is all-powerful,” McSherry said. “Even if
attackers were to come back 100 years later, with 100 years’
worth of thought and information a