Hardware Implementation of four byte per clock RC4 algorithm

In the field of cryptography till date the 2-byte in 1-clock is the best known RC4 hardware design [1], while 1-byte in 1-clock [2], and the 1-byte in 3 clocks [3][4] are the best known implementation. The design algorithm in[2] considers two consecutive bytes together and processes them in 2 clocks. The design [1] is a pipelining architecture of [2]. The design of 1-byte in 3-clocks is too much modular and clock hungry. In this paper considering the RC4 algorithm, as it is, a simpler RC4 hardware design providing higher throughput is proposed in which 6 different architecture has been proposed. In design 1, 1-byte is processed in 1-clock, design 2 is a dynamic KSA-PRGA architecture of Design 1. Design 3 can process 2 byte in a single clock, where as Design 4 is Dynamic KSA-PRGA architecture of Design 3. Design 5 and Design 6 are parallelization architecture design 2 and design 4 which can compute 4 byte in a single clock. The maturity in terms of throughput, power consumption and resource usage, has been achieved from design 1 to design 6. The RC4 encryption and decryption designs are respectively embedded on two FPGA boards as co-processor hardware, the communication between the two boards performed using Ethernet.Comment: This is an unpublished draft versio

ArchNet: Data Hiding Model in Distributed Machine Learning System

Integrating idle embedded devices into cloud computing is a promising approach to support distributed machine learning. In this paper, we approach to address the data hiding problem in such distributed machine learning systems. For the purpose of the data encryption in the distributed machine learning systems, we propose the Tripartite Asymmetric Encryption theorem and give mathematical proof. Based on the theorem, we design a general image encryption scheme ArchNet.The scheme has been implemented on MNIST, Fashion-MNIST and Cifar-10 datasets to simulate real situation. We use different base models on the encrypted datasets and compare the results with the RC4 algorithm and differential privacy policy. Experiment results evaluated the efficiency of the proposed design. Specifically, our design can improve the accuracy on MNIST up to 97.26% compared with RC4.The accuracies on the datasets encrypted by ArchNet are 97.26%, 84.15% and 79.80%, and they are 97.31%, 82.31% and 80.22% on the original datasets, which shows that the encrypted accuracy of ArchNet has the same performance as the base model. It also shows that ArchNet can be deployed on the distributed system with embedded devices