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 ) + i h 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.