Simulating Uniform Hashing in Constant Time and Optimal Space

Many algorithms and data structures employing hashing have been analyzed under the uniform hashing assumption, i.e., the assumption that hash functions behave like truly random functions. In this paper it is shown how to implement hash functions that can be evaluated on a RAM in constant time, and behave like truly random functions on any set of n inputs, with high probability. The space needed to represent a function is O(n) words, which is the best possible (and a polynomial improvement compared to previous fast hash functions). As a consequence, a broad class of hashing schemes can be implemented to meet, with high probability, the performance guarantees of their uniform hashing analysis.