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Communications of the ACM
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
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SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
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OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Privacy-Preserving String Comparisons in Record Linkage Systems: A Review
Information Security Journal: A Global Perspective
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ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
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ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
A taxonomy of privacy-preserving record linkage techniques
Information Systems
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Privacy Preserving Record Linkage (PPRL) is the scientific field that explores methods of linking datasets in order to identify common entities efficiently and accurately by simultaneously preserving the privacy of the underlying data. In this paper we present a distributed Locality Sensitive Hashing-based framework for linking huge collections of records, by grouping similar records efficiently and by distributing computations among underutilized commodity hardware resources uniformly, without imposing an extra overhead on the existing infrastructure, thus promoting scalability. We also propose two methods of assessing computational cost, aiming to distribute workload evenly among compute nodes.