Chapter 3 - robustBF: a high accuracy and memory efficient 2D Bloom Filter for diverse applications

Abstract

Bloom Filter is a crucial probabilistic data structure to reduce memory consumption for membership filters. It is applied in diverse domains such as Computer Networking, Network Security and Privacy, IoT, Edge Computing, Cloud Computing, Big Data, and Biometrics. But Bloom Filter has an issue of false positive probability. We propose a novel robust Bloom Filter, robustBF for short, to address the issue. robustBF is a 2D Bloom Filter, capable of filtering millions of data items with high accuracy without compromising the performance and memory footprint. Our proposed system is presented in two steps. Firstly, we modify the Murmur hash function, and test all modified hash functions for improvements, thus selecting the best-modified hash function experimentally. Secondly, we embed the modified hash functions into robustBF. Our experimental results show that robustBF is better than standard Bloom Filter and counting Bloom Filter in every aspect. robustBF exhibits nearly zero false positive probability with more than 10× and 44× lower memory consumption than the standard Bloom filter and counting Bloom Filter, respectively.