DROPS

Document

Invited Talk

DOI: 10.4230/LIPIcs.CSL.2022.2

How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk)

Authors: Natasha Fernandes, Annabelle McIver, and Carroll Morgan

Published in: LIPIcs, Volume 216, 30th EACSL Annual Conference on Computer Science Logic (CSL 2022)

Abstract

The study of quantitative risk in security systems is often based around complex and subtle mathematical ideas involving probabilities. The notations for these ideas can pose a communication barrier between collaborating researchers even when those researchers are working within a similar framework. This paper describes the use of geometrical representation and reasoning as a way to share ideas using the minimum of notation so as to build intuition about what kinds of properties might or might not be true. We describe a faithful geometrical setting for the channel model of quantitative information flow (QIF) and demonstrate how it can facilitate "proofs without words" for problems in the QIF setting.

Cite as

Natasha Fernandes, Annabelle McIver, and Carroll Morgan. How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk). In 30th EACSL Annual Conference on Computer Science Logic (CSL 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 216, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{fernandes_et_al:LIPIcs.CSL.2022.2,
  author =	{Fernandes, Natasha and McIver, Annabelle and Morgan, Carroll},
  title =	{{How to Develop an Intuition for Risk... and Other Invisible Phenomena}},
  booktitle =	{30th EACSL Annual Conference on Computer Science Logic (CSL 2022)},
  pages =	{2:1--2:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-218-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{216},
  editor =	{Manea, Florin and Simpson, Alex},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CSL.2022.2},
  URN =		{urn:nbn:de:0030-drops-157227},
  doi =		{10.4230/LIPIcs.CSL.2022.2},
  annote =	{Keywords: Geometry, Quantitative Information Flow, Proof, Explainability, Privacy}
}

Document

Invited Paper

DOI: 10.4230/LIPIcs.CONCUR.2020.1

On Privacy and Accuracy in Data Releases (Invited Paper)

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

Published in: LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020)

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.

Cite as

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)

Copy BibTex To Clipboard

@InProceedings{alvim_et_al:LIPIcs.CONCUR.2020.1,
  author =	{Alvim, M\'{a}rio S. and Fernandes, Natasha and McIver, Annabelle and Nunes, Gabriel H.},
  title =	{{On Privacy and Accuracy in Data Releases}},
  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 =	{Konnov, Igor and Kov\'{a}cs, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.1},
  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}
}

Search Results

Documents authored by Fernandes, Natasha

How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk)

Abstract

Cite as

On Privacy and Accuracy in Data Releases (Invited Paper)

Abstract

Cite as

Thanks for your feedback!

Could not send message