ARA : Aggregated RAPPOR and Analysis for Centralized Differential Privacy
This work addresses the problem of improving centralized differential privacy analysis for sensitive statistical data, but it appears incremental as it builds on existing RAPPOR methods.
The paper tackled the gap between local and central differential privacy approaches by proposing a model that aggregates RAPPOR reports from multiple clients using a Tf-Idf estimation to generate centralized differential privacy analysis, successfully analyzing the major truth value efficiently.
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.