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Private Search Over Big Data Leveraging Distributed File System and Parallel Processing

Authors:
Ayse Selcuk
Cengiz Orencik
Erkay Savas

Keywords: Cloud computing, Big Data, Keyword Search, Privacy, Hadoop

Abstract:
In this work, we identify the security and privacy problems associated with a certain Big Data application, namely secure keyword-based search over encrypted cloud data and emphasize the actual challenges and technical difficulties in the Big Data setting. More specifically, we provide definitions from which privacy requirements can be derived. In addition, we adapt an existing work on privacy-preserving keyword-based search method to the Big Data setting, in which, not only data is huge but also changing and accumulating very fast. Our proposal is scalable in the sense that it can leverage distributed file systems and parallel programming techniques such as the Hadoop Distributed File System (HDFS) and the MapReduce programming model, to work with very large data sets. We also propose a lazy idf-updating method that can efficiently handle the relevancy scores of the documents in a dynamically changing, large data set. We empirically show the efficiency and accuracy of the method through extensive set of experiments on real data.

Pages: 116 to 121

Copyright: Copyright (c) IARIA, 2015

Publication date: March 22, 2015

Published in: conference

ISSN: 2308-4294

ISBN: 978-1-61208-388-9

Location: Nice, France

Dates: from March 22, 2015 to March 27, 2015