  Differential privacy(DP) has now become a standard in case of sensitive
statistical data analysis. The two main approaches in DP is local and central.
Both the approaches have a clear gap in terms of data storing,amount of data to
be analyzed, analysis, speed etc. Local wins on the speed. We have tested the
state of the art standard RAPPOR which is a local approach and supported this
gap. Our work completely focuses on that part too. Here, we propose a model
which initially collects RAPPOR reports from multiple clients which are then
pushed to a Tf-Idf estimation model. The Tf-Idf estimation model then estimates
the reports on the basis of the occurrence of "on bit" in a particular position
and its contribution to that position. Thus it generates a centralized
differential privacy analysis from multiple clients. Our model successfully and
efficiently analyzed the major truth value every time.
