PROJECT TITLE :

Differentially Private Data Publishing and Analysis: a Survey - 2017

ABSTRACT:

Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially personal data publishing and differentially non-public knowledge analysis. We have a tendency to compare the various unleash mechanisms of differentially private data publishing given a selection of input information in terms of question type, the maximum variety of queries, efficiency, and accuracy. We establish two basic frameworks for differentially personal information analysis and list the typical algorithms used at intervals every framework. The results are compared and mentioned based mostly on output accuracy and efficiency. Any, we tend to propose several potential directions for future analysis and attainable applications.


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