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DOI: 10.4230/LIPIcs.CONCUR.2020.1
URN: urn:nbn:de:0030-drops-128130
URL: https://drops.dagstuhl.de/opus/volltexte/2020/12813/
Alvim, Mário S. ; Fernandes, Natasha ; McIver, Annabelle ; Nunes, Gabriel H.
On Privacy and Accuracy in Data Releases (Invited Paper)
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Abstract
In this paper we study the relationship between privacy and accuracy in the context of correlated datasets. We use a model of quantitative information flow to describe the the trade-off between privacy of individuals' data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release. We show that, where correlations exist in datasets, it is not possible to implement optimal noise-adding mechanisms that give the best possible accuracy or the best possible privacy in all situations. Finally we illustrate the trade-off between accuracy and privacy for local and oblivious differentially private mechanisms in terms of inference attacks on medium-scale datasets.
BibTeX - Entry
@InProceedings{alvim_et_al:LIPIcs:2020:12813,
author = {M{\'a}rio S. Alvim and Natasha Fernandes and Annabelle McIver and Gabriel H. Nunes},
title = {{On Privacy and Accuracy in Data Releases (Invited Paper)}},
booktitle = {31st International Conference on Concurrency Theory (CONCUR 2020)},
pages = {1:1--1:18},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-160-3},
ISSN = {1868-8969},
year = {2020},
volume = {171},
editor = {Igor Konnov and Laura Kov{\'a}cs},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2020/12813},
URN = {urn:nbn:de:0030-drops-128130},
doi = {10.4230/LIPIcs.CONCUR.2020.1},
annote = {Keywords: Privacy/utility trade-off, Quantitative Information Flow, inference attacks}
}
| Keywords: | Privacy/utility trade-off, Quantitative Information Flow, inference attacks | |
| Seminar: | 31st International Conference on Concurrency Theory (CONCUR 2020) | |
| Issue date: | 2020 | |
| Date of publication: | 26.08.2020 |
