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The Apache Hadoop framework provides parallel processing and distributed data storage capabilities that data analytics applications can utilize to process massive sets of raw data. These big data applications typically run as a set of MapReduce jobs to take advantage of Hadoop's ease of service deployment and large-scale parallelism. Yet, Hadoop has not been adapted for Multi-Level Secure (MLS) environments where data of different security classifications co-exist. To solve this problem, the authors have used the Security Enhanced Linux (SELinux) Linux kernel extension in a prototype cross-domain Hadoop on which multiple instances of Hadoop applications run at different sensitivity levels.
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