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Bloom filters

Published in Jun Wu, Rachel Wu, Yuxi Candice Wang, The Beauty of Mathematics in Computer Science, 2018

Bloom filters rely on the mathematical principle that the probability of two completely random numbers being identical is very low. Hence, with a very small false alarm rate, Bloom filters can store large amounts of information with relatively little space. A common solution to the false positives problem is to build a small whitelist to store the information that may be misidentified. Bloom filters only involve simple arithmetic, which makes them fast and easy to use.

Efficient name matching based on a fast two-dimensional filter in named data networking

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Journal Information

Published in International Journal of Parallel, Emergent and Distributed Systems, 2019

Roaa Shubbar, Mahmood Ahmadi

The main disadvantage of Bloom filters is the inability to support delete operations. This disadvantage has been addressed later by proposing a counting Bloom filter (CBF). Rather than a bit array, CBF defines an array of counters C ={} to implement the number of elements that are hashed to its positions. Consequently, insert/delete operations can be treated readily by increment and decrement the corresponding counters [33].