  Minhashing is a technique used to estimate the Jaccard Index between two sets
by exploiting the probability of collision in a random permutation. In order to
speed up the computation, a random permutation can be approximated by using an
universal hash function such as the $h_{a,b}$ function proposed by Carter and
Wegman. A better estimate of the Jaccard Index can be achieved by using many of
these hash functions, created at random. In this paper a new iterative
procedure to generate a set of $h_{a,b}$ functions is devised that eliminates
the need for a list of random values and avoid the multiplication operation
during the calculation. The properties of the generated hash functions remains
that of an universal hash function family. This is possible due to the random
nature of features occurrence on sparse datasets. Results show that the
uniformity of hashing the features is maintaned while obtaining a speed up of
up to $1.38$ compared to the traditional approach.
