LESS: Big Data Sketching and Encryption on Low Power Platform

Amey Kulkarni1, Colin Shea1, Houman Homayoun2 and Tinoosh Mohsenin1
1Department of Computer Science
2Department of Electrical and Computer Engineering, George Mason University


Ever-growing IoT demands big data processing and cognitive computing on mobile and battery operated devices. However, big data processing on low power embedded cores is challenging due to their limited communication bandwidth and on-chip storage. Additionally, IoT and cloud-based computing demand low overhead security kernel to avoid data breaches. In this paper, we propose a Light-weight Encryption using Scalable Sketching (LESS) framework for big data sketching and encryption using One-Time Random Linear Projections (OTRLP). OTRLP encoded matrix makes the Known Plaintext Attacks (KPA) ineffective, and attackers cannot gain significant information from plaintext-ciphertext pair. LESS framework can reduce data up to 67% with 3.81 dB signal-to-reconstruction error rate (SRER). This framework has two important kernels ``sketching'' and ``sketch-reconstruction'', the latter is computationally intensive and costly. We propose to accelerate the sketch reconstruction using Orthogonal Matching Pursuit (OMP) on a domain specific many-core hardware named Power Effcient Nano Cluster (PENC) designed by authors of this paper. To demonstrate effciency of LESS framework, we integrate it with Hadoop MapReduce platform for objects and scenes identification application. The full hardware integration consists of tiny ARM cores which perform task scheduling and objects identification application, while PENC acts as an accelerator for sketch reconstruction. The full hardware integration results show that the LESS framework achieves 46% reduction in data transfers with very low execution overhead of 0.11% and negligible energy overhead of 0.001% when tested for 2.6 GB streaming input data. The heterogeneous LESS framework requires 238 215; less transfer time and achieves 2.2538 215; higher throughput per watt compared to MapReduce platform.

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