A Memory-Optimized Bloom Filter Using an Additional Hashing Function

A bloom filter is a simple space-efficient randomized data structure for the representation set of items in order to support membership queries. In recent years, Bloom filters have increased in popularity in database and networking applications. In this paper, we introduce a new extension to optimize memory utilization for regular bloom filters, called bloom filter with an additional hashing function (BFAH). The regular bloom filter stores items from a set k times k memory locations that are determined by the k addresses stored in the bit-array structure. Which k addresses to utilize is determined by to which positions in the structure the k (regular) hashing functions are pointing to. Utilizing the additional hashing function, only one out of these k memory addresses is selected to store the item only once. Consequently, there is no longer needed to store the k-1 redundant copies. We implemented our approach in a software packet classifier based on tuple space search with the H3 class of universal hashing functions. Our results show that our approach is able to reduce the number of collisions when compared to a regular bloom filter.