Sliding bloom filters

Moni Naor, Eylon Yogev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A Bloom filter is a method for reducing the space (memory) required for representing a set by allowing a small error probability. In this paper we consider a Sliding Bloom Filter: a data structure that, given a stream of elements, supports membership queries of the set of the last n elements (a sliding window), while allowing a small error probability and a slackness parameter. The problem of sliding Bloom filters has appeared in the literature in several communities, but this work is the first theoretical investigation of it. We formally define the data structure and its relevant parameters and analyze the time and memory requirements needed to achieve them. We give a low space construction that runs in O(1) time per update with high probability (that is, for all sequences with high probability all operations take constant time) and provide an almost matching lower bound on the space that shows that our construction has the best possible space consumption up to an additive lower order term.

Original languageEnglish
Title of host publicationAlgorithms and Computation - 24th International Symposium, ISAAC 2013, Proceedings
Number of pages11
StatePublished - 2013
Externally publishedYes
Event24th International Symposium on Algorithms and Computation, ISAAC 2013 - Hong Kong, China
Duration: 16 Dec 201318 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8283 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Symposium on Algorithms and Computation, ISAAC 2013
CityHong Kong


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