A Joint Exponential Mechanism For Differentially Private Top-$k$

January 28, 2022 Β· Declared Dead Β· πŸ› International Conference on Machine Learning

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Authors Jennifer Gillenwater, Matthew Joseph, AndrΓ©s MuΓ±oz Medina, MΓ³nica Ribero arXiv ID 2201.12333 Category cs.CR: Cryptography & Security Citations 18 Venue International Conference on Machine Learning Last Checked 4 months ago
Abstract
We present a differentially private algorithm for releasing the sequence of $k$ elements with the highest counts from a data domain of $d$ elements. The algorithm is a "joint" instance of the exponential mechanism, and its output space consists of all $O(d^k)$ length-$k$ sequences. Our main contribution is a method to sample this exponential mechanism in time $O(dk\log(k) + d\log(d))$ and space $O(dk)$. Experiments show that this approach outperforms existing pure differential privacy methods and improves upon even approximate differential privacy methods for moderate $k$.
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