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A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this drawback when the probability of an error is sufficiently low. We

Abstract. ABloomfilter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been

Bloom filters are heavily used in the literature for efficiently representing sets with elements from a large universe. However, the current literature lacks some important functionality on bloom filters which can be proven useful in several application domains, especially in distributed and P2P

Abstract: This paper surveys the mathematics behind Bloom filters, some important variations and network-related applications of Bloom filters. The current researches show that although Bloom filters start drawing significant attention from the academic community and there has been considerable

A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information and has broad applications. In this paper, we generalize the traditional Bloom

Abstract—A Bloom filter is an effective, space-efficient data structure for concisely representing a set and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a

Abstract—A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information and has broad applica-tions. In this paper, we generalize the traditional

Abstract — A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad applications. In this paper, we generalize

Bloom Filters provide space-efficient storage of sets at the cost of a probability of false positives on mem-bership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements