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SAFE: SPARQL Federation over RDF Data Cubes with Access Control
BACKGROUND: Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288952/ https://www.ncbi.nlm.nih.gov/pubmed/28148277 http://dx.doi.org/10.1186/s13326-017-0112-6 |
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author | Khan, Yasar Saleem, Muhammad Mehdi, Muntazir Hogan, Aidan Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh |
author_facet | Khan, Yasar Saleem, Muhammad Mehdi, Muntazir Hogan, Aidan Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh |
author_sort | Khan, Yasar |
collection | PubMed |
description | BACKGROUND: Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. RESULTS: We present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. CONCLUSIONS: We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting. |
format | Online Article Text |
id | pubmed-5288952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52889522017-02-09 SAFE: SPARQL Federation over RDF Data Cubes with Access Control Khan, Yasar Saleem, Muhammad Mehdi, Muntazir Hogan, Aidan Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh J Biomed Semantics Research BACKGROUND: Several query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. RESULTS: We present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. CONCLUSIONS: We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting. BioMed Central 2017-02-01 /pmc/articles/PMC5288952/ /pubmed/28148277 http://dx.doi.org/10.1186/s13326-017-0112-6 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Khan, Yasar Saleem, Muhammad Mehdi, Muntazir Hogan, Aidan Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title | SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title_full | SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title_fullStr | SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title_full_unstemmed | SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title_short | SAFE: SPARQL Federation over RDF Data Cubes with Access Control |
title_sort | safe: sparql federation over rdf data cubes with access control |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288952/ https://www.ncbi.nlm.nih.gov/pubmed/28148277 http://dx.doi.org/10.1186/s13326-017-0112-6 |
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