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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ted Krovetz | en_US |
dc.contributor.author | Phillip Rogaway | en_US |
dc.date.accessioned | 2018-09-11T08:55:53Z | - |
dc.date.available | 2018-09-11T08:55:53Z | - |
dc.date.issued | 2006-10-16 | en_US |
dc.identifier.issn | 00200190 | en_US |
dc.identifier.other | 2-s2.0-33745661204 | en_US |
dc.identifier.other | 10.1016/j.ipl.2005.11.026 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33745661204&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/61605 | - |
dc.description.abstract | The 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.subject | Computer Science | en_US |
dc.title | Variationally universal hashing | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Information Processing Letters | en_US |
article.volume | 100 | en_US |
article.stream.affiliations | California State University Sacramento | en_US |
article.stream.affiliations | University of California, Davis | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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