Abstract:
Cloud computing platforms risk data leakage, so security and privacy are major concerns. Data can leak while in storage, processing, or moving within a cloud or between cloud infrastructures, such as private to public clouds.
This project protects “processing” data. MapReduce has been widely used in healthcare and business data analysis for big data applications. This article proposes a trust-based MapReduce framework for big data processing.
We quantify and propose data and map/reduce slot sensitive and trust values. We calculate each big data processing resource’s trust value. A task’s trust level depends on the data’s sensitivity. More sensitive data requires higher-trust servers/slots. The MapReduce scheduling problem is then the maximum weighted matching problem of a bipartite graph that maximizes the total trust value over all possible assignments subject to different task trust requirements.
It’s NP-hard. In a computing node (VM), slots share the same trust value from the secured transformation phase. This reduces weight bipartite graph slot nodes. We propose a fast heuristic algorithm that achieves 94.7 percent of the optimal solution found by exhaustive search. The trust-based scheduling scheme protects data sensitivity and improves big data application performance, according to extensive simulations.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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