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Retouched Bloom Filters: Trading Off False Positives for False Negatives

This study explores enabling networked applications to balance selected false positives against false negatives using retouched Bloom filters. It presents techniques like randomized bit clearing and selective clearing to manage troublesome false positives. Case studies on network measurement and resource routing are included. The findings indicate a neutral trade-off between false positives and false negatives with improved flexibility.

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Retouched Bloom Filters: Trading Off False Positives for False Negatives

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  1. Retouched Bloom Filters:Allowing Networked Applications to Trade Off Selected False Positives Against False Negatives • Benoit Donnet • Joint work with Bruno Baynat and Timur Friedman • CoNEXT 2006, Lisbon 1

  2. Context • Bloom filters are introduced in 1970 ([Bloom]) • Set membership problem • Trade-off between space and computing complexity • Lossy summary technique • Historical usage • Spell checking ([McIlroy]) • Database ([Bratbergsengen]) • Networking usage • See ([Broder & Mitzenmacher])

  3. Bloom filters

  4. Bloom filters (2)

  5. Contributions • Removing false positives at the expense of generating false negatives • Retouched Bloom filters • Randomized bit clearing • Selective clearing • Case study

  6. Motivations • Some false positives might be more troublesome than others • Network measurement • Peer-2-peer, overlay • Resource routing • Network packet processing

  7. Motivations (2)

  8. Motivations (3) • An application can tolerate a low level of false negatives • Can we trade-off the most troublesome false positives for some randomly false negatives? • Retouched Bloom filters

  9. RBF

  10. RBF (2)

  11. Randomized bit clearing • Quid if we randomly reset bits in the vector? • False positives or not • Randomized bit clearing • Resetting s bits in the vector to 0 • Eliminates the same proportion of false positives as the proportion of false negatives generated

  12. Selective Clearing • Focus on troublesome false positives • Four algorithms • Random Selection • Minimum FN Selection • Maximum FP Selection • Ratio Selection

  13. Selective clearing (2)

  14. Case study • Route tracing with a red stop set (RSS) of penultimate nodes • RSS implementation • List • Bloom filter • RBF • Skitter data from Jan. 2006 • Ten monitors • 10,000 destinations

  15. Case study (2)

  16. Case study (3)

  17. Case study (4)

  18. Conclusion • Retouched Bloom filters • Flexibility • The trade-off between false positives and false negatives is, at worst, neutral • Selective clearing • Case study

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