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On Privacy and Accuracy in Data Releases (Invited Paper)

Authors Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel H. Nunes

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Author Details

Mário S. Alvim
  • Computer Science Department, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brasil
Natasha Fernandes
  • Department of Computing, Macquarie University, Sydney, Australia
Annabelle McIver
  • Department of Computing, Macquarie University, Sydney, Australia
Gabriel H. Nunes
  • Computer Science Department, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brasil

Funding

  • Alvim, Mário S.: Supported by CNPq, CAPES, and FAPEMIG.
  • Nunes, Gabriel H.: Supported by CNPq, CAPES, and FAPEMIG.

Cite AsGet BibTex

Mário S. Alvim, Natasha Fernandes, Annabelle McIver, and Gabriel H. Nunes. On Privacy and Accuracy in Data Releases (Invited Paper). In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 1:1-1:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.CONCUR.2020.1

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.

Subject Classification

ACM Subject Classification
  • Security and privacy
Keywords
  • Privacy/utility trade-off
  • Quantitative Information Flow
  • inference attacks

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References

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