Theory and Practice of Bloom Filters for Distributed Systems
By Sasu Tarkoma, Christian Esteve Rothenberg, E. Lagerspetz. IEEE Communications Surveys and Tutorials, 2012
520 citations recorded by the source corpus.
Many network solutions and overlay networks utilize probabilistic techniques to reduce information processing and networking costs. This survey article presents a number of frequently used and useful probabilistic techniques. Bloom filters and their variants are of prime importance, and they are heavily used in various distributed systems. This has been reflected in recent research and many new algorithms have been proposed for distributed systems that are either directly or indirectly based on Bloom filters. In this survey, we give an overview of the basic and advanced techniques, reviewing over 20 variants and discussing their application in distributed systems, in particular for caching, peer-to-peer systems, routing and forwarding, and measurement data summarization.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/8dd55802ca52cb74aad86254edc3f2722cff8f4b
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In Artificial Intelligence
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In Computer Networks and Communications
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Citations in the corpus
- "Better Than Nothing" Privacy with Bloom Filters: To What Extent? (2012, methodology): “We recall from well known Bloom filter results [6, 8]”
- Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy (2022, background): “See Tarkoma et al. (2011) for the full calculations, which the ones listed here are taken from.”