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Documents authored by McIver, Annabelle

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)

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@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)

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@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}
}
Document
DOI: 10.4230/DagRep.5.4.123
Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)

Authors: Gilles Barthe, Andrew D. Gordon, Joost-Pieter Katoen, and Annabelle McIver

Published in: Dagstuhl Reports, Volume 5, Issue 4 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 15181 "Challenges and Trends in Probabilistic Programming". Probabilistic programming is at the heart of machine learning for describing distribution functions; Bayesian inference is pivotal in their analysis. Probabilistic programs are used in security for describing both cryptographic constructions (such as randomised encryption) and security experiments. In addition, probabilistic models are an active research topic in quantitative information now. Quantum programs are inherently probabilistic due to the random outcomes of quantum measurements. Finally, there is a rapidly growing interest in program analysis of probabilistic programs, whether it be using model checking, theorem proving, static analysis, or similar. Dagstuhl Seminar 15181 brought researchers from these various research communities together so as to exploit synergies and realize cross-fertilisation.
Cite as

Gilles Barthe, Andrew D. Gordon, Joost-Pieter Katoen, and Annabelle McIver. Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181). In Dagstuhl Reports, Volume 5, Issue 4, pp. 123-141, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{barthe_et_al:DagRep.5.4.123,
  author =	{Barthe, Gilles and Gordon, Andrew D. and Katoen, Joost-Pieter and McIver, Annabelle},
  title =	{{Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)}},
  pages =	{123--141},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{4},
  editor =	{Barthe, Gilles and Gordon, Andrew D. and Katoen, Joost-Pieter and McIver, Annabelle},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.4.123},
  URN =		{urn:nbn:de:0030-drops-53536},
  doi =		{10.4230/DagRep.5.4.123},
  annote =	{Keywords: Bayesian networks, differential privacy, machine learning, probabilistic programs, security, semantics, static analysis, verification}
}
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