Signal Processing Oriented Approach for Big Data Privacy
This paper addresses the challenge of big data security by exploitingsignal processing theories. We propose a new big data privacy protocol that scrambles data via artificial noise and secret transform matrices. The utility of the scrambled data is maintained, as demonstrated by a cyber-physical system application. We further outline the proof of the proposed protocol’s privacy by considering the limitations of blind source separation and compressive sensing
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