NDLI: Privacy-Preserving Layer over MapReduce on Cloud

Content Provider	IEEE Xplore Digital Library
Author	Xuyun Zhang Chang Liu Nepal, S. Wanchun Dou Jinjun Chen
Copyright Year	2012
Abstract	Cloud computing provides powerful and economical infrastructural resources for cloud users to handle everincreasing Big Data with data-processing frameworks such as MapReduce. Based on cloud computing, the MapReduce framework has been widely adopted to process huge-volume data sets by various companies and organizations due to its salient features. Nevertheless, privacy concerns in MapReduce are aggravated because the privacy-sensitive information scattered among various data sets can be recovered with more ease when data and computational power are considerably abundant. Existing approaches employ techniques like access control or encryption to protect privacy in data processed by MapReduce. However, such techniques fail to preserve data privacy cost-effectively in some common scenarios where data are processed for data analytics, mining and sharing on cloud. As such, we propose a flexible, scalable, dynamical and costeffective privacy-preserving layer over the MapReduce framework in this paper. The layer ensures data privacy preservation and data utility under the given privacy requirements before data are further processed by subsequent MapReduce tasks. A corresponding prototype system is developed for the privacy-preserving layer as well.
Starting Page	304
Ending Page	310
File Size	422314
Page Count	7
File Format	PDF
ISBN	9781467330275
DOI	10.1109/CGC.2012.43
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Privacy Cloud computing Privacy preservation Data handling Big Data Information management Cryptography Framework Data storage systems MapReduce
Content Type	Text
Resource Type	Article

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