On the false-positive rate of Bloom filters

Authors:
Prosenjit Bose;Hua Guo;Evangelos Kranakis;Anil Maheshwari;Pat Morin;Jason Morrison;Michiel Smid;Yihui Tang
Affiliations:
Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada;Carleton University, School of Computer Science, 1125 Colonel by Drive, Ottawa, Ontario, Canada
Venue:
Information Processing Letters
Year:
2008

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Abstract

Bloom filters are a randomized data structure for membership queries dating back to 1970. Bloom filters sometimes give erroneous answers to queries, called false positives. Bloom analyzed the probability of such erroneous answers, called the false-positive rate, and Bloom's analysis has appeared in many publications throughout the years. We show that Bloom's analysis is incorrect and give a correct analysis.