Search Results for author: Ryan M. Rogers

Found 1 papers, 1 papers with code

Practical Differentially Private Top-k Selection with Pay-what-you-get Composition

1 code implementation NeurIPS 2019 David Durfee, Ryan M. Rogers

We design algorithms that ensures (approximate) differential privacy and only needs access to the true top-k' elements from the data for any chosen k' ≥ k. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain.

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