Title :
Signal Processing Oriented Approach for Big Data Privacy
Author :
Xiaohua Li ; Yang, Tao
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York at Binghamton, Binghamton, NY, USA
Abstract :
This paper addresses the challenge of big data security by exploiting signal 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.
Keywords :
Big Data; compressed sensing; data privacy; matrix algebra; security of data; Big Data privacy; Big Data security; artificial noise; blind source separation; compressive sensing; secret transform matrix; signal processing; Big data; Data privacy; Noise; Power demand; Protocols; Vectors; big data; cyber-physical systems; privacy; signal processing;
Conference_Titel :
High Assurance Systems Engineering (HASE), 2015 IEEE 16th International Symposium on
Conference_Location :
Daytona Beach Shores, FL
Print_ISBN :
978-1-4799-8110-6
DOI :
10.1109/HASE.2015.23