"How to Make Your Approximation Algorithm Private: A Black-Box ..."

Jeremiah Blocki et al. (2023)

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DOI: 10.4230/LIPICS.APPROX/RANDOM.2023.59

access: open

type: Conference or Workshop Paper

metadata version: 2025-03-03

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  - conf/approx/BlockiGMZ23
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Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee, Samson Zhou:
How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity. APPROX/RANDOM 2023: 59:1-59:24

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