Abstract:We provide an end-to-end Renyi DP based-framework for differentially private top-k selection. Unlike previous approaches, which require a data-independent choice on k, we propose to privately release a data-dependent choice of k such that the gap between k-th and the (k + 1)st "quality" is large. This is achieved by a novel application of the Report-Noisy-Max. Not only does this eliminate one hyperparameter, the adaptive choice of k also certifies the stability of the top-k indices in the unordered set so we c… Show more
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