Ternary Bloom Filter Replacing Counting Bloom Filter

Lim, Hyesook; Lee, Jungwon; Byun, Hayoung; Yim, Changhoon

doi:10.1109/lcomm.2016.2624286

Cited by 37 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A general drawback of standard BF is that it fails to delete elements from the bit vector, since resetting the bits from 1 to 0 may cause false negative errors to other elements. Besides of the CBF [36], Deletable BF [133] and Ternary BF [134] are proposed to enable the deletion of arbitrary element.…”

Section: A Element Deletionmentioning

confidence: 99%

“…Ternary BF (TBF). Ternary BF [134] allocates the minimum number of bits to each counter and thereby implies more counters. Each counter in the proposed TBF has three different values: 0, 1, and X.…”

Section: A Element Deletionmentioning

confidence: 99%

“…Multi-class BF [3] Optihash [67] FPF-MBF [43] Retouched BF [75] MPCBF [77] Ternary BF [134] Generalized BF [76] Cross-checking BF [72] Complement BF [73] Yes-no BF [74] VI-CBF [83] FP-CBF [84] Selected Hash [78] [79] Space-code BF [88] Adaptive BF [114] Spectral BF [112] Loglog BF [113] Dynamic BF [89] Variable length signatures [117] Scalable BF [118] DBA [90] Par-BF [91] Weighted BF [119] Popularity conscious BF [120] BloomStore [17] kBF [19] IBLT [21] k-mer BF [131] Spatial BF [17] CBF [36] Deletable BF [133] Distance-sensitive BF [143] Locality-sensitive BF [144] Less hash [85] DLB-BF [96] Combinatorial BF [97] Bloom-1 [92] OMASS [93] Compressed BF [99] Compacted BF [100] Forest-structured BF [106] Stable BF [135] Temporal CBF [136] d-left CBF [101] Memo. Opti.…”

Section: Bit Vectormentioning

confidence: 99%

“…Ternary BF [134] doesn't reset the bits to 0s, on the contrary, it records the bits which incur hash collision and marks their value as X. When querying an element y, if the corresponding k counters of y are all X, then y is identified as indeterminable.…”

Section: Techniques Towards the Bit Vectormentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

View full text Add to dashboard Cite

Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

show abstract

Section: A Element Deletionmentioning

confidence: 99%

Section: A Element Deletionmentioning

confidence: 99%

Section: Bit Vectormentioning

confidence: 99%

Section: Techniques Towards the Bit Vectormentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

View full text Add to dashboard Cite

show abstract

“…Another popular research topic is to improve the performance of the SBF via modifications of the original approach. The ternary BF [17] improves the performance of the CBF as it only allows three possible values of each position. The deletable BF [18] uses additional positions in the filter, which are used to support the deletion of elements from the filter without introducing false negatives.…”

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

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

Distributed SCFMBF Based Protocol for Integrity In Cloud Storage System (DPICS)

2020

View full text Add to dashboard Cite

Cloud storage allowed the data owners to store their data without the burden of local hardware. There were some security concerns because of the cloud being untrusted. To make sure the stored data was intact since it was impractical to download the full data, a security mechanism named integrity check was used. Integrity check was achieved by probabilistic structures such as bloom filter and cuckoo filter. Uploading data file on to one cloud server could be a point of failure, instead the file could be distributed over l servers. Distributed SCFMBF Based Protocol for Integrity in Cloud Storage System (DPICS) was proposed to remotely check data integrity in a more effective way. Smart Cuckoo Filter Modified by Bloom Filter (SCFMBF) was a protocol that improved some of the performance properties of cuckoo filter such as false positive rate and insertion performance and solved some drawbacks of the cuckoo algorithm such as endless loop. Main characteristic of the bloom filter was used to improve cuckoo filter as the protocol name describes. First bloom filter was used as the integrity check method in cloud but bloom filter was not suitable for dynamic environment, SCFMBF was substituted for bloom filter because it had low false positive probability in integrity check and could cope well with dynamic data. How DPICS was more efficient than other previous works, was shown in terms of false positive probability, integrity check and retrievability.

show abstract

Ternary Bloom Filter Replacing Counting Bloom Filter

Cited by 37 publications

References 7 publications

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

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

Distributed SCFMBF Based Protocol for Integrity In Cloud Storage System (DPICS)

Contact Info

Product

Resources

About