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Weaknesses of Cuckoo Hashing with a Simple Universal Hash Class: The Case of Large Universes
2009Cuckoo 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
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Efficient Strongly Universal and Optimally Universal Hashing
1999New 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
2018Digital 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
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Evolving universal hash functions using genetic algorithms
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, 2009In 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.
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Fast universal quantum gate above the fault-tolerance threshold in silicon
Nature, 2022Akito Noiri +2 more
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Enhancing global access to cancer medicines
Ca-A Cancer Journal for Clinicians, 2020Javier Cortes +2 more
exaly
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Nature Machine Intelligence, 2021Lu Lu, Pengzhan Jin, Guofei Pang
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