Less Hashing, Same Performance: Building a Better Bloom Filter

Adam Kirsch, Michael Mitzenmacher

Abstract: A standard technique from the hashing literature is to use two hash functions h 1(x) and h 2(x) to simulate additional hash functions of the form g i (x) = h 1(x) + ih 2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.