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A bounded-noise mechanism for differential privacy
Yuval Dagan · Gil Kur

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.

Author Information

Yuval Dagan (Weizmann Institute of Science)
Gil Kur (Weizmann Institute of Science)