Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/61605
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dc.contributor.authorTed Krovetzen_US
dc.contributor.authorPhillip Rogawayen_US
dc.date.accessioned2018-09-11T08:55:53Z-
dc.date.available2018-09-11T08:55:53Z-
dc.date.issued2006-10-16en_US
dc.identifier.issn00200190en_US
dc.identifier.other2-s2.0-33745661204en_US
dc.identifier.other10.1016/j.ipl.2005.11.026en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33745661204&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/61605-
dc.description.abstractThe strongest well-known measure for the quality of a universal hash-function family H is its being ε-strongly universal, which measures, for randomly chosen h ∈ H, one's inability to guess h (m′) even if h (m) is known for some m ≠ m′. We give example applications in which this measure is too weak, and we introduce a stronger measure for the quality of a hash-function family, ε-variationally universal, which measures one's inability to distinguish h (m′) from a random value even if h (m) is known for some m ≠ m′. We explain the utility of this notion and provide an approach for constructing efficiently computable ε-VU hash-function families. © 2006 Elsevier B.V. All rights reserved.en_US
dc.subjectComputer Scienceen_US
dc.titleVariationally universal hashingen_US
dc.typeJournalen_US
article.title.sourcetitleInformation Processing Lettersen_US
article.volume100en_US
article.stream.affiliationsCalifornia State University Sacramentoen_US
article.stream.affiliationsUniversity of California, Davisen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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