@InProceedings{blocki_et_al:LIPIcs.APPROX/RANDOM.2023.59,
author = {Blocki, Jeremiah and Grigorescu, Elena and Mukherjee, Tamalika and Zhou, Samson},
title = {{How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity}},
booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
pages = {59:1--59:24},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-296-9},
ISSN = {1868-8969},
year = {2023},
volume = {275},
editor = {Megow, Nicole and Smith, Adam},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.59},
URN = {urn:nbn:de:0030-drops-188849},
doi = {10.4230/LIPIcs.APPROX/RANDOM.2023.59},
annote = {Keywords: Differential privacy, approximation algorithms}
}