Differential privacy data Aggregation Optimizing Method and application to data visualization

Abstract

This article explores the challenges in data privacy within the big data era with specific focus on differential privacy of social media data and its geospatial realization within a Cloud-based research environment. By using differential privacy method, this paper achieves the distortion of the data by adding noise to protect data privacy. Furthermore, this article presents the IDP k-means Aggregation Optimizing Method to decrease the overlap and superposition of massive data visualization. Finally this paper combines IDP k-means Aggregation Optimizing Method with differential privacy method to protect data privacy. The outcome of this research is a set of underpinning formal models of differential privacy that reflect the geospatial tools challenges faced with location-based information, and the implementation of a suite of Cloud-based tools illustrating how these tools support an extensive range of data privacy demands.