Is Bloom Filter a Bad Choice for Security and Privacy?

Patgiri, Ripon; Nayak, Sabuzima; Muppalaneni, Naresh Babu

doi:10.1109/icoin50884.2021.9333950

Cited by 13 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (8) gives optimal false positive probability for given m bits array and n input items (15). Now, the value of k is replaced in Equation ( 7), we get Equation (9).…”

Section: Bloom Filtermentioning

confidence: 99%

“…Bloom Filter [20] is an approximate set membership filtering data structure which is defined in Definition 1. It is extremely popular in diverse domains, particularly, Big Data [34], IoT, Cloud Computing, Networking [35], Security [21,36], Database, Bioinformatics [37], and Biometrics. Bloom Filter is applied to reduce the main memory footprint.…”

Section: Bloom Filtermentioning

confidence: 99%

See 1 more Smart Citation

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

Nayak

Patgiri

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Biological network represents the interaction or relationship between the biological entities such as proteins and genes of a biological process. A biological network with thousands of millions of vertices makes its processing complex and challenging. In this article, we have proposed a novel Bloom Filter for biological networks, called BionetBF, to provide fast membership identification of the biological network edges or paired biological data. BionetBF is capable of executing millions of operations within a second on datasets having millions of paired biological data while occupying tiny amount of main memory. We have conducted rigorous experiments to prove the performance of BionetBF with large datasets. The experiment is conducted using 12 generated datasets and three biological network datasets. BionetBF demonstrates higher performance while maintaining a 0.001 false positive probability. BionetBF is also compared with other filters: Cuckoo Filter and Libbloom, where BionetBF proves its supremacy by exhibiting higher performance with a smaller sized memory compared with large sized filters of Cuckoo Filter and Libbloom.

show abstract

“…Equation (8) gives optimal false positive probability for given m bits array and n input items (15). Now, the value of k is replaced in Equation ( 7), we get Equation (9).…”

Section: Bloom Filtermentioning

confidence: 99%

Section: Bloom Filtermentioning

confidence: 99%

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

Nayak

Patgiri

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Counting Bloom Filter become popular due to false negative free. Deletion introduces false negatives in conventional Bloom Filter [25], but deletion is the most important operation in many applications. Therefore, diverse variants of counting Bloom Filter have been introduced, and Luo et al [33] reported 15 counting Bloom Filters till 24 December 2018.…”

Section: B Frequency Countmentioning

confidence: 99%

“…Murmur hash function is the best non-cryptographic string hash functions [24]. There are also cryptographic string hash functions, however, it does not enhances the performance and the false positive probability [25]. Therefore, we compare countBF with SBF and CBF to evaluate the characteristics using various test cases.…”

Section: Introductionmentioning

confidence: 99%

countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter

Nayak¹,

Patgiri²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Bloom Filter is a probabilistic data structure for the membership query, and it has been intensely experimented in various fields to reduce memory consumption and enhance a system's performance. Bloom Filter is classified into two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter. CBF has a higher false positive probability than standard Bloom Filter (SBF), i.e., CBF uses a higher memory footprint than SBF. But CBF can address the issue of the false negative probability. Notably, SBF is also false negative free, but it cannot support delete operations like CBF. To address these issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom Filter, called countBF. countBF uses a modified murmur hash function to enhance its various requirements, which is experimentally evaluated. Our experimental results show that countBF uses 1.96× and 7.85× less memory than SBF and CBF respectively, while preserving lower false positive probability and execution time than both SBF and CBF. The overall accuracy of countBF is 99.999921, and it proves the superiority of countBF over SBF and CBF. Also, we compare with other state-of-the-art counting Bloom Filters.

show abstract

“…Besides, Bloom Filter implements hash functions to generate a separate digest for every element representation efficiently and uniquely. So, data entry to and data extraction from the filter based on the hashing requires linear complexity of time O(1) [11].…”

Section: Introductionmentioning

confidence: 99%

eBF: An Enhanced Bloom Filter for Intrusion Detection in IoT

Gebretsadik

Nayak²,

Patgiri³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Intrusion detection is an essential process to identify malicious incidents and continuously alert the many users of the Internet of Things (IoT). The constant monitoring of events generated from more than millions of devices connected to the IoT and the extensive analysis of every event based on predefined security policies consumes enormous resources. Accordingly, performance enhancement is a crucial concern of intrusion detection in IoT and other massive Big Data Systems to ensure a secure environment efficiently. Like many Big Data systems, intrusion detection system of the IoT need to employ the fast membership filter, Bloom Filter, to quickly identify possible attacks. Bloom Filter is an admiringly fast and space-efficient data structure that quickly handle elements of extensive datasets in a small memory space. However, the trade-off between the query performance and the number of hash functions, and memory space and false positive probability remain issues of Bloom Filter. Thus, this article presents an enhanced Bloom Filter (eBF) that remarkably improves memory efficiency and introduces new techniques to accelerate the speed of search processing demand of intrusion detection system in IoT. We experimentally show the efficacy of eBF using real dataset of intrusion detection. The experimental result shows that the proposed filter is remarkably memory efficient, faster, and more accurate than the state-of-the-art filters. eBF requires 15x, 13x, and 8x less memory compared with Standard Bloom Filter, Cuckoo filter, and robustBF, respectively. Therefore, this new system will significantly impact the enhancement of the performance of intrusion detection of IoT that concurrently monitors several billion events crosschecking with the defined security policies.

show abstract

Is Bloom Filter a Bad Choice for Security and Privacy?

Cited by 13 publications

References 12 publications

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter

eBF: An Enhanced Bloom Filter for Intrusion Detection in IoT

Contact Info

Product

Resources

About