Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.datak.2005.06.003
DC FieldValue
dc.titleMasking page reference patterns in encryption databases on untrusted storage
dc.contributor.authorMa, X.
dc.contributor.authorPang, H.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T07:34:05Z
dc.date.available2013-07-04T07:34:05Z
dc.date.issued2006
dc.identifier.citationMa, X., Pang, H., Tan, K.-L. (2006). Masking page reference patterns in encryption databases on untrusted storage. Data and Knowledge Engineering 58 (3) : 466-483. ScholarBank@NUS Repository. https://doi.org/10.1016/j.datak.2005.06.003
dc.identifier.issn0169023X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39106
dc.description.abstractTo support ubiquitous computing, the underlying data have to be persistent and available anywhere-anytime. The data thus have to migrate from devices that are local to individual computers, to shared storage volumes that are accessible over open network. This potentially exposes the data to heightened security risks. In particular, the activity on a database exhibits regular page reference patterns that could help attackers learn logical links among physical pages and then launch additional attacks. We propose two countermeasures to mitigate the risk of attacks initiated through analyzing the shared storage server's activity for those page patterns. The first countermeasure relocates data pages according to which page sequences they are in. The second countermeasure enhances the first by randomly prefetching pages from predicted page sequences. We have implemented the two countermeasures in MySQL, and experiment results demonstrate their effectiveness and practicality. © 2005 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.datak.2005.06.003
dc.sourceScopus
dc.subjectPage reference patterns
dc.subjectPattern mining
dc.subjectPlausible deniability
dc.subjectTraffic analysis
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.datak.2005.06.003
dc.description.sourcetitleData and Knowledge Engineering
dc.description.volume58
dc.description.issue3
dc.description.page466-483
dc.description.codenDKENE
dc.identifier.isiut000239263100011
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