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Datanami: Whittling Down MapReduce; ‘Efficient Algorithm’ Proposed to Process K-Query and Meet ‘Differential Privacy’ Need
Created by RG Maddox on 11/19/2012 12:50:27 PM

Researchers at Renmin University of China in Beijing find MapReduce “is useful, but there is a significant risk to leak out users' personal information, especially when the data is sensitive, for example, including users' health records, salary information, etc. To counter this, the concept of differential privacy has recently emerged as a new paradigm for preserving private data, which makes it possible to provide strong theoretical guarantees on the privacy and utility of the query results. The researchers propose “an efficient algorithm, called DiffMR Differentially private Top-kquery over MapReduce), for processing top-k query as well as satisfying differential privacy.”

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