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DOI: 10.4230/LIPIcs.ITCS.2023.54
URN: urn:nbn:de:0030-drops-175574
URL: https://drops.dagstuhl.de/opus/volltexte/2023/17557/
Ghazi, Badih ; Kumar, Ravi ; Manurangsi, Pasin ; Steinke, Thomas
Algorithms with More Granular Differential Privacy Guarantees
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Abstract
Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. We study several basic data analysis and learning tasks in this framework, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).
BibTeX - Entry
@InProceedings{ghazi_et_al:LIPIcs.ITCS.2023.54,
author = {Ghazi, Badih and Kumar, Ravi and Manurangsi, Pasin and Steinke, Thomas},
title = {{Algorithms with More Granular Differential Privacy Guarantees}},
booktitle = {14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
pages = {54:1--54:24},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-263-1},
ISSN = {1868-8969},
year = {2023},
volume = {251},
editor = {Tauman Kalai, Yael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/17557},
URN = {urn:nbn:de:0030-drops-175574},
doi = {10.4230/LIPIcs.ITCS.2023.54},
annote = {Keywords: Differential Privacy, Algorithms, Per-Attribute Privacy}
}
| Keywords: | Differential Privacy, Algorithms, Per-Attribute Privacy | |
| Seminar: | 14th Innovations in Theoretical Computer Science Conference (ITCS 2023) | |
| Issue date: | 2023 | |
| Date of publication: | 01.02.2023 |
