Efficient Bloom filter for network protocols using AES instruction set

Zhang, Yao; Zheng, Zhiming; Zhang, Xiao

doi:10.1049/iet-com.2016.0641

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the number of positions with value v is unknown but it is possible to calculate the probability P r(I = v) of such position in CBF using (8). Then the expected number of such positions in CBF is determined via (11) and equals mP r(I = v). When the ABF suppresses all such positions each of them decrements the expected dot product by v/n.…”

Section: Probabilistic Characterization Of the Autoscaling Bloom Filtermentioning

confidence: 99%

See 1 more Smart Citation

Autoscaling Bloom filter: controlling trade-off between true and false positives

Kleyko

Rahimi

Gayler³

et al. 2019

Neural Comput & Applic

View full text Add to dashboard Cite

A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of the performance as well as give a procedure for minimization of the false positive rate.

show abstract

Section: Probabilistic Characterization Of the Autoscaling Bloom Filtermentioning

confidence: 99%

“…In order to improve the speed of the membership check, the authors in [10] proposed a novel filter type called Ultra-Fast BFs. In [11] it was shown that BFs can be accelerated (in terms of processing speed) by using particular types of hashing functions.…”

Section: Introductionmentioning

confidence: 99%

Autoscaling Bloom filter: controlling trade-off between true and false positives

Kleyko

Rahimi

Gayler³

et al. 2019

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The BFSV entries indexed by these signatures are set to 1, while the remaining entries keep the default value of 0. Non‐cryptographic hash functions, such as the universal type [17] and Murmur [18], are generally utilised in BFs, although some cryptographic hash functions have also been effectively used for BFs in existing works [19]. Two target strings may share all, some, or no signatures.…”

Section: Bfs and False Positive Ratementioning

confidence: 99%