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Title: Approximate query processing using multilayered data model to handle environmental constraints, privacy and avoiding inferences
Author (s): Narayanan, Muthukumar
Madria, Sanjay
Clair, Dan S. T.
Department/Lab Affiliations: Computer Science
Intelligent Systems Center
Keywords: Access Control
Concept Hierarchy
Inference
MLDB
Query Processing
Summary Databases
Issue Date: 2007-06
Publisher: World Scientific Publishing
Citation: Narayanan, Muthukumar., Madria, Sanjay Kumar., and Clair, Dan S. T. "Approximate Query Processing Using Multilayered Data Model to Handle Environmental Constraints, Privacy and Avoiding Inferences.", International Journal of Cooperative Information Systems, vol. 16, no. 2, 2007.
Abstract: In this paper, we describe a query approximation system which uses the Multi-Layered Database (MLDB), a collection of summarized relational data generated using domain-based concept hierarchies. The system generates approximate answers to queries to handle environmental constraints and access control levels, thus preserving the privacy and security of data. Using concept hierarchy (CH), we generalize attributes to transform base relations to different layers of summarized relations corresponding to access control levels. The summary databases thus formed are the compression of the tuples in the main database using the CH constructed using the domain set. The query is rewritten by traversing the MLDB layers according to the user's access control level. We present summarization methods, query rewriting algorithms, implementation and experimental results of the system. In addition, we analyze some of the known inferences in Multi Level Secure (MLS) databases and then proceed to explore their effectson an approximate query processor that uses the MLDB model. The common relationships among inferential queries are found by analyzing them, and are used in possible solutions to detect and prevent inference problems. These patches are added to the query processor in MLDB to form a system that provides approximate results by preserving privacy and at the same time block the possible inferences. We have observed that these extra patches introduce only very small overheads in the MLDB generation and query processing.
Type: Article - Journal
text
In Title: International Journal of Cooperative Information Systems
Copyright Notice: Pre-print: author can archive; Post-print: author can archive;
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
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Publisher URL:
http://dx.doi.org/10.1142/S0218843007001627
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titleApproximate query processing using multilayered data model to handle environmental constraints, privacy and avoiding inferences
contributor.authorNarayanan, Muthukumar
contributor.authorMadria, Sanjay
contributor.authorClair, Dan S. T.
contributor.deptlabComputer Science
contributor.deptlabIntelligent Systems Center
subjectAccess Control
subjectConcept Hierarchy
subjectInference
subjectMLDB
subjectQuery Processing
subjectSummary Databases
date.issued2007-06
publisherWorld Scientific Publishing
identifier.citationNarayanan, Muthukumar., Madria, Sanjay Kumar., and Clair, Dan S. T. "Approximate Query Processing Using Multilayered Data Model to Handle Environmental Constraints, Privacy and Avoiding Inferences.", International Journal of Cooperative Information Systems, vol. 16, no. 2, 2007.
identifier.pub.URI
http://dx.doi.org/10.1142/S0218843007001627
description.abstractIn this paper, we describe a query approximation system which uses the Multi-Layered Database (MLDB), a collection of summarized relational data generated using domain-based concept hierarchies. The system generates approximate answers to queries to handle environmental constraints and access control levels, thus preserving the privacy and security of data. Using concept hierarchy (CH), we generalize attributes to transform base relations to different layers of summarized relations corresponding to access control levels. The summary databases thus formed are the compression of the tuples in the main database using the CH constructed using the domain set. The query is rewritten by traversing the MLDB layers according to the user's access control level. We present summarization methods, query rewriting algorithms, implementation and experimental results of the system. In addition, we analyze some of the known inferences in Multi Level Secure (MLS) databases and then proceed to explore their effectson an approximate query processor that uses the MLDB model. The common relationships among inferential queries are found by analyzing them, and are used in possible solutions to detect and prevent inference problems. These patches are added to the query processor in MLDB to form a system that provides approximate results by preserving privacy and at the same time block the possible inferences. We have observed that these extra patches introduce only very small overheads in the MLDB generation and query processing.
typeArticle - Journal
type.DCMITypetext
rightsPre-print: author can archive; Post-print: author can archive;
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rights.URI
http://www.worldscinet.com/authors/authorrights.shtml
relation.isPartOfInternational Journal of Cooperative Information Systems
date.available2008-06-11T20:04:15Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/ApproximateQueryProcessingusingMultilayeredDat_09007dcc8050ec7e.html