The veracious counting bloom filter

Counting Bloom Filters (CBFs) are widely employed in many applications for fast membership queries. CBF works on dynamic sets rather than a static set via item insertions and deletions. CBF allows false positive, but not false negative. The Bh-Counting Bloom Filter (Bh-CBF) and Variable Increment Counting Bloom Filter (VI-CBF) are introduced to reduce the false positive probability, but they suffer from memory overhead and ha rdware complexity. In this paper, we proposed a multilevel optimization approach named as Veracious B h-Counting Bloom Filter (VBh-CBF) and Veracious Variable increment Counting Bloom Filter (VVI-CBF) by partitioning the counter vector into multiple levels to reduce the False Positive Probability (FPP) and to limit the memory requirement. The experiment result shows that the false positive probability and total memory size are reduced by 65.4%, 67.74% and 20.26% , 41.29% respectively compared to basic Bh-CBF and VI-CBF.