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In this paper, we present a framework which relies on the Map/Reduce paradigm  in order to distribute computations among underutilized commodity hardware  resources uniformly, without imposing an extra overhead on the existing  infrastructure. The volume of the distance computations, required for records  comparison, is largely reduced by utilizing the so-called Locality-Sensitive  Hashing technique, which is optimally tuned in order to avoid highly  redundant computations. Experimental results illustrate the effectiveness of  our distributed framework in finding the matched record pairs in voluminous  data sets.

A distributed near-optimal LSH-based framework for privacy-preserving record linkage