License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CSL.2022.2
URN: urn:nbn:de:0030-drops-157227
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/15722/
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Fernandes, Natasha ; McIver, Annabelle ; Morgan, Carroll

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

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LIPIcs-CSL-2022-2.pdf (2 MB)

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.

BibTeX - Entry

@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/opus/volltexte/2022/15722},
  URN =		{urn:nbn:de:0030-drops-157227},
  doi =		{10.4230/LIPIcs.CSL.2022.2},
  annote =	{Keywords: Geometry, Quantitative Information Flow, Proof, Explainability, Privacy}
}

Keywords: Geometry, Quantitative Information Flow, Proof, Explainability, Privacy
Collection: 30th EACSL Annual Conference on Computer Science Logic (CSL 2022)
Issue Date: 2022
Date of publication: 27.01.2022

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