A bounded-noise mechanism for differential privacy
Yuval Dagan ⋅ Gil Kur
Abstract
We present an asymptotically optimal (epsilon, δ)-private mechanism for answering multiple, adaptively asked, ∆-sensitive queries, settling the conjecture of Steinke and Ullman [2020]. Our algorithm adds independent noise of bounded magnitude to each query, while prior solutions relied on unbounded noise such as the Laplace and Gaussian mechanisms.
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