Malicious Website Detection Using Probabilistic Data Structure Bloom Filter

2019 ◽  
Author(s):  
K. Nandhini ◽  
Ramesh Balasubramaniam
Keyword(s):  
Data Structure ◽  
Bloom Filter ◽  
Probabilistic Data ◽  
Probabilistic Data Structure

scholarly journals Creating a Concurrent Overflowing Bloom Filter

2019 ◽  
Author(s):  
Alex Berliner ◽  
Brian Estes ◽  
Ebin Scaria
Keyword(s):  
Data Structure ◽  
Recent Literature ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Additional Element ◽  
Marginal Value ◽  
The Creation ◽  
Probabilistic Data Structure

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

scholarly journals Creating a Concurrent Overflowing Bloom Filter

2019 ◽  
Author(s):  
Alex Berliner ◽  
Brian Estes ◽  
Ebin Scaria
Keyword(s):  
Data Structure ◽  
Recent Literature ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Additional Element ◽  
Marginal Value ◽  
The Creation ◽  
Probabilistic Data Structure

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

A Load Balancing Mechanism Using Bloom Filter in Storm System

2014 ◽  
Vol 543-547 ◽  
pp. 1972-1976
Author(s):  
Huai Lin Dong ◽  
Ming Yuan He ◽  
Qing Feng Wu ◽  
Sheng Hang Wu
Keyword(s):  
Data Structure ◽  
Load Balancing ◽  
Real Time ◽  
Data Transmission ◽  
Streaming Media ◽  
Bloom Filter ◽  
Time System ◽  
Real Time System ◽  
Membership Queries ◽  
Probabilistic Data Structure

When membership queries are evaluated in a set, the performance can be improved by a Bloom filter which is a space-efficient probabilistic data structure. According to its space-efficient character, Bloom Filter presented to address the load balancing problem for streaming media information in Storm system which is free and open source distributed real time computation system. This method increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves real time system Storm efficiently in saving the data transmission time and reducing the calculation complexity.

scholarly journals These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101271 ◽  
Author(s):  
Qingpeng Zhang ◽  
Jason Pell ◽  
Rosangela Canino-Koning ◽  
Adina Chuang Howe ◽  
C. Titus Brown
Keyword(s):  
Data Structure ◽  
Probabilistic Data ◽  
Probabilistic Data Structure

scholarly journals Integrated Probabilistic Data Structure For Accurate and Scalable Sequence Prediction

2020 ◽  
Vol 167 ◽  
pp. 2429-2436
Author(s):  
Soumonos Mukherjee ◽  
Uddipta Dutta ◽  
Jit Sarkar ◽  
Rajkumar R
Keyword(s):  
Data Structure ◽  
Probabilistic Data ◽  
Sequence Prediction ◽  
Probabilistic Data Structure

scholarly journals Privacy-Enhanced Robust Image Hashing with Bloom Filters

2021 ◽  
Author(s):  
Uwe Breidenbach ◽  
Martin Steinebach ◽  
Huajian Liu
Keyword(s):  
Data Structure ◽  
Privacy Protection ◽  
Structural Information ◽  
Bloom Filter ◽  
Error Rates ◽  
Bloom Filters ◽  
Image Hashing ◽  
Robust Image ◽  
Probabilistic Data Structure ◽  
Robust Image Hashing

Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure -- known as Bloom filter -- to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.

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