On privacy and accuracy in data releases

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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.

Original languageEnglish
Title of host publication31st International Conference on Concurrency Theory
Subtitle of host publicationCONCUR 2020, September 1–4, 2020, Vienna, Austria (Virtual Conference)
EditorsIgor Konnov, Laura Kovács
Place of PublicationSaarbrücken/Wadern, Germany
PublisherDagstuhl Publishing
Number of pages18
ISBN (Electronic)9783959771603
Publication statusPublished - Aug 2020
Event31st International Conference on Concurrency Theory, CONCUR 2020 - Virtual, Vienna, Austria
Duration: 1 Sep 20204 Sep 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference31st International Conference on Concurrency Theory, CONCUR 2020
CityVirtual, Vienna

Bibliographical 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.


  • Privacy/utility trade-off
  • Quantitative Information Flow
  • Inference attacks

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