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arXiv:2002.03057cs.DS

The Bloom Tree

Lum Ramabaja, Arber Avdullahu

Abstract

We introduce a data structure that allows for efficient (probabilistic) presence proofs and non-probabilistic absence proofs in a bandwidth efficient and secure way. The Bloom tree combines the idea of Bloom filters with that of Merkle trees. Bloom filters are used to verify the presence, or absence of elements in a set. In the case of the Bloom tree, we are interested to verify and transmit the presence, or absence of an element in a secure and bandwidth efficient way to another party. Instead of sending the whole Bloom filter to check for the presence, or absence of an element, the Bloom tree achieves efficient verification by using a compact Merkle multiproof.

arXiv comments
Journal reference
DOI
Subjects
cs.DSData Structures and Algorithms cs.CRCryptography and Security

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Feb 2020 to Jul 2021. Found in 1 HN submission from Feb 2020 to Feb 2020. Open submission discussions.

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Discussed in comments2 threads

    • I realize I wasn't clear. I used 2 sqlite databases just to store the article titles and offsets into binary files (<2GB) that had chunks of compressed data. Each chunk contained 10s-100s of stories, so that the decompressed around was around 20megs I believe. I had planned to…

      sitkackView on HNParent context

  1. The Compact Merkle Multiproof 1 comment
    • You still need one hash for every non-leaf node, so 30-40 hashes of 256 bits each. What this algorithm saves is 30-40 indices, which is 11 bits each for a 1000-leaf tree. Even aligning to 16 bits it's doesn't seem a great optimization. Unless I misunderstood something and we are…

      krczView on HNParent context

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