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Algorithms for Constructing Anonymizing Arrays

Attribute-based methods are inherently identity-less as authorization decisions are made in terms of attributes possessed by the subject rather than identity. However, anonymity against the system is not guaranteed when attribute distribution allows for the composition of a policy that few subjects...

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Detalles Bibliográficos
Autores principales: Lanus, Erin, Colbourn, Charles J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254900/
http://dx.doi.org/10.1007/978-3-030-48966-3_29
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author Lanus, Erin
Colbourn, Charles J.
author_facet Lanus, Erin
Colbourn, Charles J.
author_sort Lanus, Erin
collection PubMed
description Attribute-based methods are inherently identity-less as authorization decisions are made in terms of attributes possessed by the subject rather than identity. However, anonymity against the system is not guaranteed when attribute distribution allows for the composition of a policy that few subjects can satisfy. An anonymizing array ensures that any assignment of values to t attributes that appears in the array appears at least r times. When an anonymizing array is used for subjects registered to a system and policies contain conjunctions of at most t attributes, the system cannot identify the subject using the policy to to gain authorization with greater than [Formula: see text] probability. Anonymizing arrays are similar to covering arrays with higher coverage and constraints, but have an additional desired property, homogeneity, due to their application domain. In this paper, we develop constructions for anonymizing arrays and propose a post-optimization mechanism to reduce homogeneity.
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spelling pubmed-72549002020-05-28 Algorithms for Constructing Anonymizing Arrays Lanus, Erin Colbourn, Charles J. Combinatorial Algorithms Article Attribute-based methods are inherently identity-less as authorization decisions are made in terms of attributes possessed by the subject rather than identity. However, anonymity against the system is not guaranteed when attribute distribution allows for the composition of a policy that few subjects can satisfy. An anonymizing array ensures that any assignment of values to t attributes that appears in the array appears at least r times. When an anonymizing array is used for subjects registered to a system and policies contain conjunctions of at most t attributes, the system cannot identify the subject using the policy to to gain authorization with greater than [Formula: see text] probability. Anonymizing arrays are similar to covering arrays with higher coverage and constraints, but have an additional desired property, homogeneity, due to their application domain. In this paper, we develop constructions for anonymizing arrays and propose a post-optimization mechanism to reduce homogeneity. 2020-04-30 /pmc/articles/PMC7254900/ http://dx.doi.org/10.1007/978-3-030-48966-3_29 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Lanus, Erin
Colbourn, Charles J.
Algorithms for Constructing Anonymizing Arrays
title Algorithms for Constructing Anonymizing Arrays
title_full Algorithms for Constructing Anonymizing Arrays
title_fullStr Algorithms for Constructing Anonymizing Arrays
title_full_unstemmed Algorithms for Constructing Anonymizing Arrays
title_short Algorithms for Constructing Anonymizing Arrays
title_sort algorithms for constructing anonymizing arrays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254900/
http://dx.doi.org/10.1007/978-3-030-48966-3_29
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