Sampling and Reconstruction Using Bloom Filters - 2018


During this Project, we address the problem of sampling from a set and reconstructing a collection stored as a Bloom filter. To the most effective of our information our work is the first to handle this question. We introduce a novel hierarchical data structure called BloomSampleTree that helps us style economical algorithms to extract an nearly uniform sample from the set stored during a Bloom filter and additionally permits us to reconstruct the set efficiently. Within the case where the hash functions used in the Bloom filter implementation are partially invertible, in the way that it's straightforward to calculate the set of elements that map to a explicit hash value, we have a tendency to propose a second, a lot of house-efficient technique called HashInvert for the reconstruction. We tend to study the properties of these 2 ways each analytically furthermore experimentally. We tend to offer bounds on run times for each ways and sample quality for the BloomSampleTree based mostly algorithm, and show through an intensive experimental evaluation that our methods are efficient and effective.

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