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In this paper the authors introduce a framework for privacy preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the following properties: Provably strong privacy; Adequate efficiency at reasonably large scale; and Robustness against realistic adversaries. The framework gains its practicality by decomposing data mining algorithms into a sequence of vector addition steps that can be privately evaluated using a new Verifiable Secret Sharing (VSS) scheme over small field (e.g., 32 or 64 bits), which has the same cost as regular, non-private arithmetic.
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