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Our daily life, with the knowledge revolution has entered into a major transformation. For the last few years, with the concept of "internet of things", experts agree that this transformation will affect every part of society and the concept of business. However, this rapid transformation has many problems. One of the most important problems is how...
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... Swierczewski 8 implemented the 3DES algorithm on GPUs using CUDA with considerable improvements over the CPU versions. Guler et al. 9 implemented the original DES algorithm using CUDA for lightweight systems. They also observed a significant performance increase over the conventional implementation. ...
Triple DES (3DES) is a NIST and ISO/IEC standard block cipher that is also used in some web browsers and several electronic payment applications. We propose an optimized bit‐level parallelization of 3DES for GPU accelerated encryption to allow processing high volumes of data. Since the block size of 3DES is 64 bits, our approach considers a kernel block as a 64‐bit 3DES block. Each kernel block performs XOR, permutation, and S‐box operations of this cipher in parallel and memory accesses are optimized by the use of constant and shared memory. Although table based and bitsliced implementations of block ciphers on GPUs outperform naive implementations, their performance vary significantly on different GPU models and architectures. Lack of publicly available source codes prohibit a fair comparison of the performance results for different implementations. In this work, we provide performance results on various GPU models and make our implementation publicly available for reproducibility and further comparisons. When compared against the baseline multi‐threaded CPU implementation, our optimization achieves an average of 15.95 speed‐up when encrypting large files using an RTX 2070 Super GPU. Moreover, when modified into a key search attack, more than 94.4 million 3DES key searches per second can be conducted on an RTX 2070 Super GPU.
... However, since the devices used in IoT applications have various resource constraints, more efficient algorithms are required for both processing and memory requirements. Güler et al. [19] have implemented the DES algorithm on CUDA to study the improvements that could be made on performance. The experimental results showed that the obtained design is more efficient than the original DES algorithm. ...
Steganography is one of the best techniques to hide secret data. Several steganography methods are available that use an image as a cover object, which is called image steganography. In image steganography, the major features are the cover object quality and hiding data capacity. Due to poor image quality, attackers could easily hack the secret data. Therefore, the hidden data quantity should be improved, while keeping stego-image quality high. The main aim of this study is combining several steganography techniques, for secure transmission of data without leakage and unauthorized access. In this paper, a technique, which combines various steganographybased techniques, is proposed for secure transmission of secret data. In the pre-processing step, resizing of cover image is performed with Pixel Repetition Method (PRM). Then DES (Data Encryption Standard) algorithm is used to encrypt secret data before embedding it into cover image.The encrypted data is then converted to hexadecimal representation. This is followed by embedding using Least Signification Bit (LSB) in order to hide secret data inside the cover image. Further, image de-noising using Convolutional Neural Network (CNN) is used to enhance the cover imagewith hidden encrypted data.Embedded Zerotrees of Wavelet Transform is used to compress the image in order to reduce its size. Experiments are conducted to evaluate the performance of proposed combined steganography technique and results indicate that the proposed technique outperforms all existing techniques. It achieves better PSNR, and encryption/decryption times, than existing methods for medical and other types of images.
