A standard technique from the hashing literature is to use two hash functions h1(x)
and h2(x)to simulate additional hash functions of the form gi(x) = h1(x)+ih2(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.
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