Abstract
Differential privacy (DP) has now become a standard in case of sensitive statistical data analysis. The two main approaches in DP are 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.
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https://github.com/Suvixx/ARA-Aggregated-RAPPOR-and-Analysis-for-Centralized-Differential-Privacy.
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This article is part of the topical collection “Advances in Internet Research and Engineering” guest edited by Mohit Sethi, Debabrata Das, P. V. Ananda Mohan and Balaji Rajendran.
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Paul, S., Mishra, S. ARA: Aggregated RAPPOR and Analysis for Centralized Differential Privacy. SN COMPUT. SCI. 1, 22 (2020). https://doi.org/10.1007/s42979-019-0023-y
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DOI: https://doi.org/10.1007/s42979-019-0023-y