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Bloom filters are a randomized data structure for membership queries, but they sometimes give erroneous answers, called false positives. The original analysis of the false-positive rate by Bloom is incorrect, and a correct analysis is provided. The false-positive rate is the probability that a query returns "yes" for an element not in the filter. The original proof of the false-positive rate is flawed, assuming independence between events that are not independent. A counterexample shows that the original bound underestimates the false-positive rate. The paper provides an exact formula for the false-positive rate, an upper bound that converges to the original bound for most common parameter choices, and shows that the original bound is a strict lower bound for any k ≥ 2.

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Bloom filters are a randomized data structure for membership queries, but they sometimes give erroneous answers, called false positives.

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  • File Format: PDF
  • File Size: 132 KB
  • Pages: 6
  • Language: EN
  • Total Downloads: 220
  • Last Updated: 1 month ago

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