CS-Based Secured Big Data Processing on FPGA

Abstract

The four V's in Big data sets, Volume, Velocity, Variety, and Veracity, provides challenges in many different aspects of real-time systems. Out of these areas securing big data sets, reduction in processing time and communication bandwidth are of utmost importance. In this paper we adopt Compressive Sensing (CS) based framework to address all three issues. We implement compressive Sensing using Deterministic Random Matrix (DRM) on Artix-7 FPGA, and CS reconstruction using Orthogonal Matching Pursuit (OMP) algorithm on Virtex-7 FPGA. The results show that our implementations for CS sampling and reconstruction are 183x and 2.7x respectively faster when compared to previously published work. We also perform case study of two different applications i.e. multi-channel Seizure Detection and Image processing to demonstrate the efficiency of our proposed CS-based framework. CS-based framework allows us to reduce communication transfers up to 75% while achieving satisfactory range of quality. The results show that our proposed framework is 290x faster and has 7.9x less resource utilization as compared to previously published AES based encryption.