License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CONCUR.2020.1
URN: urn:nbn:de:0030-drops-128130
URL: https://drops.dagstuhl.de/opus/volltexte/2020/12813/
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Alvim, Mário S. ; Fernandes, Natasha ; McIver, Annabelle ; Nunes, Gabriel H.

On Privacy and Accuracy in Data Releases (Invited Paper)

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LIPIcs-CONCUR-2020-1.pdf (0.6 MB)


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
Collection: 31st International Conference on Concurrency Theory (CONCUR 2020)
Issue Date: 2020
Date of publication: 26.08.2020


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