Abstract
Many applications utilize Probabilistic Data Structure (PDS) to reduce data storage and data processing cost. PDS use probabilistic approaches and approximation principles along with hashing techniques for fast processing of data. In recent years, they have been gaining much popularity due to the fact that they can be efficiently used for big data processing and streaming applications. A Bloom filter is a probabilistic data structure that supports representation of a set S of N elements in very low space and support set membership testing. As compared to original set space requirement of Bloom filter is very low. Bloom filter finds application in many domains of Computer Science. In this survey, several variants and applications of Bloom filter in different domains have been discussed.
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