Linear Hash Functions

Noga Alon, Martin Dietzfelbinger, Peter Bro Miltersen, Erez Petrank, Gábor Tardos

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Consider the set H of all linear (or affine) transformations between two vector spaces over a finite field F. We study how good H is as a class of hash functions, namely we consider hashing a set S of size n into a range having the same cardinality n by a randomly chosen function from H and look at the expected size of the largest hash bucket. H is a universal class of hash functions for any finite field, but with respect to our measure different fields behave differently. If the finite field F has n elements, then there is a bad set S ∩ F2 of size n with expected maximal bucket size Ω(n1/3). If n is a perfect square, then there is even a bad set with largest bucket size always at least √n. (This is worst possible, since with respect to a universal class of hash functions every set of size n has expected largest bucket size below √n + 1/2.) If, however, we consider the field of two elements, then we get much better bounds. The best previously known upper bound on the expected size of the largest bucket for this class was O(2√log n). We reduce this upper bound to O(log n log log n). Note that this is not far from the guarantee for a random function. There, the average largest bucket would be 0(log nl log log n). In the course of our proof we develop a tool which may be of independent interest. Suppose we have a subset S of a vector space D over Z2, and consider a random linear mapping of D to a smaller vector space R. If the cardinality of S is larger than cε|R|Iog|R|, then with probability 1 - ε, the image of S will cover all elements in the range.

Original languageEnglish (US)
Pages (from-to)667-683
Number of pages17
JournalJournal of the ACM
Volume46
Issue number5
DOIs
StatePublished - Jan 1 1999
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Hardware and Architecture
  • Artificial Intelligence

Keywords

  • Hashing via linear maps
  • Universal hashing

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  • Cite this

    Alon, N., Dietzfelbinger, M., Miltersen, P. B., Petrank, E., & Tardos, G. (1999). Linear Hash Functions. Journal of the ACM, 46(5), 667-683. https://doi.org/10.1145/324133.324179