Survey of Computation Integrity Methods For Big Data

Nowadays, big data becomes widespread. Big
data has great value, but it faces many challenges. One of
these challenges is security. Many classic security techniques
exist, but these mechanisms are not appropriate for big data
security. To secure big data, it is necessary to secure many
aspects such as infrastructure, data privacy, data
management, and integrity and reactive. Securing
computations in distributed programming frameworks and
protecting non-relational data stores are two requirements
for infrastructure protection. This survey will highlight
securing MapReduce as one of the most popular distributed
programming frameworks. Security of MapReduce
computation is an important consideration when a
MapReduce computation is performed on a public or
hybrid cloud. When a MapReduce job is executed on
public cloud or hybrid cloud, an integrity check of its result
is required. In this survey, a set of previous techniques that
check the result integrity of MapReduce will be explained. In
addition to discussion of the advantages and disadvantages
of each technique.