@inproceedings{3cff06a5e6174759b37f93a15f15b539,
title = "On privacy and accuracy in data releases",
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.",
keywords = "Privacy/utility trade-off, Quantitative Information Flow, Inference attacks",
author = "Alvim, {M{\'a}rio S.} and Natasha Fernandes and Annabelle McIver and Nunes, {Gabriel H.}",
note = "Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. ; 31st International Conference on Concurrency Theory, CONCUR 2020 ; Conference date: 01-09-2020 Through 04-09-2020",
year = "2020",
month = aug,
doi = "10.4230/LIPIcs.CONCUR.2020.1",
language = "English",
volume = "171",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Dagstuhl Publishing",
pages = "1--18",
editor = "Igor Konnov and Laura Kov{\'a}cs",
booktitle = "31st International Conference on Concurrency Theory",
address = "Germany",
}