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Weaknesses of Cuckoo Hashing with a Simple Universal Hash Class: The Case of Large Universes

2009
Cuckoo hashing was introduced by Pagh and Rodler in 2001 [12]. A set S of n keys is stored in two tables T 1 and T 2 each of which has m cells of capacity 1 such that constant access time is guaranteed. For m ≥ (1 + e)n and hash functions h 1, h 2 that are c logn-wise independent, Pagh [11] showed that the keys of an arbitrary set S can be stored using
Martin Dietzfelbinger, Ulf Schellbach
openaire   +1 more source

Efficient Strongly Universal and Optimally Universal Hashing

1999
New hash families are analyzed, mainly consisting of the hash functions ha,b : {0,..., u - 1} → {0,..., r - 1}, x → ((ax + b) mod(kr)) div k. Universal classes of such functions have already been investigated in [5, 6], and used in severail applications, e.g. [3,9].
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Efficient Unconditionally Secure Signatures Using Universal Hashing

2018
Digital signatures are one of the most important cryptographic primitives. In this work we construct an information-theoretically secure signature scheme which, unlike prior schemes, enjoys a number of advantageous properties such as short signature length and high generation efficiency, to name two.
Ryan Amiri   +3 more
openaire   +1 more source

Evolving universal hash functions using genetic algorithms

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009
In this paper we explore the use of metaheuristic functions, namely Genetic Algorithms to construct Universal Hash Functions to efficiently hash a given set of keys. The Hash Functions generated in this way should give lesser number of collisions as compared to selecting them randomly from a family of Universal Hash Functions.
openaire   +1 more source

Fast universal quantum gate above the fault-tolerance threshold in silicon

Nature, 2022
Akito Noiri   +2 more
exaly  

Enhancing global access to cancer medicines

Ca-A Cancer Journal for Clinicians, 2020
Javier Cortes   +2 more
exaly  

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Nature Machine Intelligence, 2021
Lu Lu, Pengzhan Jin, Guofei Pang
exaly  

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