Date of Publication :7th October 2016

Abstract: Map Reduce is a programming prototype that enables for huge scalability across hundreds or thousands of servers in a Hadoop.. Map Reduce is extensively used daily around the world as an efficient distributed computation tool for a huge class of problems such as search, clustering, log analysis, different types of join operations, pattern matching, matrix multiplication and analysis of social networks. Privacy and security of data and Map Reduce computations are significant concerns when a Map Reduce computation is implements in public or hybrid clouds. In order to perform a Map Reduce functions in hybrid and public clouds, authentication of mappers-reducers, privacy of data-computations, Integrity and reliability of data-computations and freshness-correctness of the outputs are mandatory. Satisfying these necessities defend the operation from a number of types of attacks on data and Map Reduce computations. In this Security and privacy challenges and needs, considering a range of adversarial capabilities, and characteristics within the scope of Map Reduce. We presented security and privacy protocols for Map Reduce and talk about their transparency problems.

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