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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/117870...
Part of book or chapter of book . 2006 . Peer-reviewed
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Generalized Compact Knapsacks Are Collision Resistant

Authors: Vadim Lyubashevsky; Daniele Micciancio;

Generalized Compact Knapsacks Are Collision Resistant

Abstract

In (Micciancio, FOCS 2002), it was proved that solving the generalized compact knapsack problem on the average is as hard as solving certain worst-case problems for cyclic lattices. This result immediately yielded very efficient one-way functions whose security was based on worst-case hardness assumptions. In this work, we show that, while the function proposed by Micciancio is not collision resistant, it can be easily modified to achieve collision resistance under essentially the same complexity assumptions on cyclic lattices. Our modified function is obtained as a special case of a more general result, which yields efficient collision-resistant hash functions based on the worst-case hardness of various new problems. These include new problems from algebraic number theory as well as classic lattice problems (e.g., the shortest vector problem) over ideal lattices, a class of lattices that includes cyclic lattices as a special case

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
217
Top 1%
Top 0.1%
Top 10%
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