Scalable Bloom Filters

Research output: Contribution to journalArticlepeer-review

183 Citations (Scopus)


Bloom filters provide space-efficient storage of sets at the cost of a probability of false positives on membership 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 without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.
Original languageUnknown
Pages (from-to)255-2611
JournalInformation Processing Letters
Issue number6
Publication statusPublished - 1 Jan 2007

Cite this