2026-07-28T21:01:23Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12813 2024-03-06T10:50:45Z ddc:004 open_access

On Privacy and Accuracy in Data Releases (Invited Paper) Alvim, Mário S. Fernandes, Natasha McIver, Annabelle Nunes, Gabriel H. Privacy/utility trade-off Quantitative Information Flow inference attacks 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Mário S. Alvim and Natasha Fernandes and Annabelle McIver and Gabriel H. Nunes 2020 Is Part Of LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CONCUR.2020.1 urn:nbn:de:0030-drops-128130 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.1 eng https://creativecommons.org/licenses/by/3.0/legalcode