Files cryptography based on one-time pad algorithm

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it’s successful without losing any information.

A Huffman-Based Joint Compression and Encryption Scheme for Secure Data Storage Using Physical Unclonable Functions

With the developments of Internet of Things (IoT) and cloud-computing technologies, cloud servers need storage of a huge volume of IoT data with high throughput and robust security. Joint Compression and Encryption (JCAE) scheme based on Huffman algorithm has been regarded as a promising technology to enhance the data storage method. Existing JCAE schemes still have the following limitations: (1) The keys in the JCAE would be cracked by physical and cloning attacks; (2) The rebuilding of Huffman tree reduces the operational efficiency; (3) The compression ratio should be further improved. In this paper, a Huffman-based JCAE scheme using Physical Unclonable Functions (PUFs) is proposed. It provides physically secure keys with PUFs, efficient Huffman tree mutation without rebuilding, and practical compression ratio by combining the Lempel-Ziv and Welch (LZW) algorithm. The performance of the instanced PUFs and the derived keys was evaluated. Moreover, our scheme was demonstrated in a file protection system with the average throughput of 473Mbps and the average compression ratio of 0.5586. Finally, the security analysis shows that our scheme resists physical and cloning attacks as well as several classic attacks, thus improving the security level of existing data protection methods.

Performance comparison of RSA and AES to SMS messages compression using Huffman algorithm

Improved security of short message services (SMS) can be obtained using cryptographic methods, both symmetric and asymmetric, but must remain efficient. This paper aims to study the performance and efficiency of the symmetric crypto of AES-128 and asymmetric crypto of RSA with message compression in securing SMS messages. The ciphertext of RSA and AES were compressed using the Huffman algorithm. The average AES encryption time for each character is faster than RSA, which is 5.8 and 24.7 ms/character for AES and AES+Huffman encryption and 8.7 and 45.8 ms/character for RSA and RSA+Huffman, from messages with 15, 30, 60 and 90 characters. AES decryption time is also faster, which is 27.2 ms/character compared to 47.6 ms/character in RSA. Huffman compression produces an average efficiency of 24.8 % for the RSA algorithm, better than 17.35 % of AES efficiency for plaintext of 1, 16, 45, and 88 characters.

Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.

Comparison Analysis with Huffman Algorithm and Goldbach Codes Algorithm in File Compression Text Using the Method Exponential Comparison

With the development of technology at this time many people know about compression. In simple compression is a process to shrink the file from its original size. At this time compression applications that are often used are WinZip, WinRar, and 7-Zip, namely with the aim of compressing documents and saving space on memory or data transmission. Compressed data can be in the form of images, audio, video and text. The use of the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is intended to provide enormous benefits in the sending and storage process and requires less memory space compared to uncompressed text. The algorithm starts by providing a string of inputs as input, how to produce an algorithm output in the form of a binary string or code that translates each input string, so that the string has a small number of bits compared to strings that are not compressed. Thus, the problem is how to obtain the code with sorted characters and frequency tables as input and shorter binary code as output. In applying the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is very good, the results were not reduced from the original file or there was no reduction

Hyperspectral regression lossless compression algorithm of aerospace images

In this work, we propose an algorithm for compressing lossless hyperspectral aerospace images, which is characterized by the use of a channel-difference linear regression transformation, which significantly reduces the range of data changes and increases the degree of compression. The main idea of the proposed conversion is to form a set of pairs of correlated channels with the subsequent creation of the transformed blocks without losses using regression analysis. This analysis allows you to reduce the size of the channels of the aerospace image and convert them before compression. The transformation of the regressed channel is performed on the values of the constructed regression equation model. An important step is coding with the adapted Huffman algorithm. The obtained comparison results of the converted hyperspectral AI suggest the effectiveness of the stages of regression conversion and multi-threaded processing, showing good results in the calculation of compression algorithms.