Less hashing, same performance: Building a better Bloom filter
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. (C) 2008 Wiley Periodicals, Inc.
bloom filters double hashing poisson convergence