A Bloom Filter with the Integrated Hash Table Using an Additional Hashing Function

Abstract

A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. In recent years, Bloom filters have increased in popularity in database and networking applications. A Bloom filter has two steps that called programming and membership query. In this paper, we introduce a new approach to integrate a hash table (HT) with Bloom filter to decrease the HT access time. This means that when a Bloom filter for an incoming item is programmed, the incoming item simultaneously is stored in a HT. In addition in the membership query step, if the query is successful, simultaneously the address of item in the HT is generated. Furthermore, we analyze the average bucket size, maximum search length and number of collisions for the proposed approach and compare to the fast hash table (FHT) approach. 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 average bucket size, maximum search length and number of collisions when compared to a FHT.

BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

“…In Ahmadi et al [32], BF are simple and space-efficient randomized data structures used to represent a certain group, such that any connection requests are maintained. Recently, the acceptance ability in their database and networking applications has been improved.…”