Huffman Coding Research Articles

Efficient lossless based secure communication in 6G Internet-of-Things environments

Future sustainable energy-efficient computing solutions in e-healthcare, smart cities, and intelligent robotics applications provide the many benefits and real-time services made possible by the Internet of Things (IoT) and the cloud computing platform. IoT is a relatively new technology expected to emerge due to the expansion of data, communication channels, and embedded systems. Through the IoT, users and physical objects exchange data and interact on virtual networks. Due to the resource-constraint nature of IoT, it is becoming increasingly difficult for organizations and individuals to protect their private information as the value of digital information rises. Reversible Data Hiding (RDH) in Encrypted Images (RDHEI) is gaining popularity in 6G technology for privacy protection because of its ability to embed secret data within an encrypted image and confirm the hidden information authenticity. In this research, we proposed a sustainable, energy-efficient, multi-MSB (most significant bit)-based dynamic quadtree partition with enhanced Huffman coding. Different prediction representations are also utilized to anticipate the existing pixel value from the adjacent pixels based on quadtree partitions to take greater advantage of the spatial association in the cover image. Furthermore, the freed-up large space is used to insert additional data using dynamic quadtree partitions to encrypt the image. Our approach has an optimum embedding capacity compared to the current state-of-the-art schemes.

Image lossless compression algorithm optimization and FPGA implementation

In this thesis, a minimum redundant prefix coding with higher compression ratio and lower time complexity is proposed for lossless compression of HD images. The compression algorithm is based on Canonical Huffman coding, preprocesses the data source to be compressed according to the image data features, and then compresses the data in batches using the locally uneven features in the data, which improves the compression ratio by 1.678 times compared with the traditional canonical Huffman coding. During the implementation of the algorithm, the counting sorting method with lower time complexity and the code-length table construction method without relying on binary trees are used to reduce the complexity of the algorithm and achieve the purpose of real-time data processing by the system. Finally, the proposed compression algorithm is deployed in FPGA to improve the encoding rate by parallel hardware circuit and pipeline design.

Low calculation cost of HEVC coding unit size based on spatial homogeneity detection

For the same video quality, HEVC gives 25% to 50% bitrate savings, compared to its predecessor the Advanced Video Coding H.264 and thus supports resolutions up to 8 K UHD. However, the reduction in bitrates provided by the HEVC leads to an increase in the computational cost of the encoding operation. This complexity can become a true handicap especially for real-time video streaming and also for VANET (Vehicular Ad-Hoc Network) applications such as traffic safety and Video surveillance. The improvement in the bitrates and also the increase in the calculation cost are due to the use of large and multi-sized coding, prediction and transform blocks. Indeed, the H264 coder is based on structure macroblocks with sizes 4 × 4, 8 × 8 and 16 × 16, while H.265 depends on Coding Tree Units (CTUs), CTUs select sizes 4 × 4, 8 × 8, 16 × 16, 32 × 32 and 64 × 64 blocks. This paper proposes a fast CU (Coding Unit) size decision method to reduce the HEVC calculation cost based on spatial homogeneity. Compared with the HM16.13 benchmark test model, the average coding time is reduced by around 40% for CIF / QCIF video sequences, 35% to 43% for class A, B and C test sequences. These important reductions in coding time are obtained with negligible loss of quality and an average increase in bitrates which does not exceed 0.89% for the three configuration modes (All intra, Random Access and Low Delay).

Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography Technique

Since the Internet is a medium for transporting sensitive data, the privacy of the message transported has become a major concern. Image steganography has become a prominent tool for hiding data to ensure privacy during transfer. An efficient steganography system is essential to accomplish the best embedding capacity and maintain the other parameters at a satisfying level. Image encryption systems provide a secure and flexible system to maintain the privacy of image conversion and storage in the transmission system. Many existing image steganography methods can be attacked by various techniques, or do not support many image formats for embedding. To resolve these shortcomings, this study presents the Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography (HEWTEA-IS) technique, aiming to achieve secrecy with no compromise in image quality. The HEWTEA-IS method uses Discrete Wavelet Transform (DWT) for the decomposition of images into different subbands, and Huffman encoding to determine the embedding bits on the decomposed blocks and offer an additional layer of security. Moreover, the WTEA resolves the problem of imperceptibility by identifying the optimal probable position in the cover image for embedding secret bits. The proposed algorithm was simulated and examined in terms of different measures, and an extensive experimental analysis ensured that it is superior to other methods in several aspects.

Protein Sequence Comparison Method Based on 3-ary Huffman Coding

Based on 3-ary Huffman coding algorithm, we propose a digital mapping method of protein sequence. Firstly, a 3-ary Huffman tree is defined by the frequency characteristic of 20 amino acids in given protein sequences. The 0-2 codes of 20 amino acids constructed by the 3-ary Huffman tree can convert long protein sequences into one-to-one 0-2 digital sequences. According to the frequency characteristic and the distribution information of 0-2 codes of 20 amino acids in the 0-2 digital sequences, we design the 40-dimensional vectors to characterize the protein sequences. Next, the proposed digital mapping method is used to perform three separate applications, similarity comparison of nine ND6 proteins, evolutionary trend analysis of the 2009 pandemic Human influenza A (H1N1) viruses from January 2020 to June 2022, and the evolution analysis of 95 coronavirus genes. The results illustrate the utility of the proposed method.

Improving Code Completion by Solving Data Inconsistencies in the Source Code with a Hierarchical Language Model

In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-the-art language models to deal with the data inconsistency problem, which is caused by the free naming conventions of source code. It is common for user-defined variables or methods with similar semantics in source code, to have different names in different projects. This means that a model trained on one project may encounter many words the model has never seen before during another project. Those freely named variables or functions in the code will bring difficulties to the processes of training and prediction and cause a data inconsistency problem between projects. However, we discover that the syntax tree of source code has hierarchical structures. This code structure has strong regularity in different projects and can be used to combat data inconsistency. In this paper, we propose a novel Hierarchical Language Model (HLM) to improve the robustness of the state-of-the-art recurrent language model, in order to be able to deal with data inconsistency between training and testing. The newly proposed HLM takes the hierarchical structure of the code tree into consideration to predict code. The proposed HLM method generates the embedding for each sub-tree according to hierarchies and collects the embedding of each sub-tree in context, to predict the next piece of code. The experiments on inner-project and cross-project datasets indicate that the newly proposed HLM method performs better than the state-of-the-art recurrent language model in dealing with the data inconsistency between training and testing, and achieves an average improvement in prediction accuracy of 11.2%.

Secure Electronic Commerce Transactions Using Pythagorean Triple-based Cryptography and Audio Steganography

The rapidly increasing utilization of Information Technology (IT) in electronic commerce and other aspects of human existence has led to a rise in worries about the privacy and safe-ty/security of those involved. Due to the alarmingly high frequency with which data or user privacy breaches occur today, conducting online transactions can represent a major threat to buyers (and even business owners') privacy. Existing solutions to these security issues are still vulnerable to classic phishing scams. As a result, the construction of a robust and effi-cient audio steganography system for secure electronic commerce transactions is presented in this work. The developed system first encrypts sensitive financial data using the Pythagorean Triple-based Cryptographic (PTC) algorithm, then compresses the resulting cipher-text using the Lempel Ziv Welch (LZW) and Huffman's Compression Algorithms, and finally embeds the compressed file (using one-dimensional Discrete Cosine Transform (DCT)) in a suitable audio-file cover to produce an output (stego file) that is indistinguishable from the cover file. This output can then be exchanged between communicating entities across open networks. The new system has a little or no alteration in its outputs, as evidenced by the Signal-to-Noise Ratio (SNR), in the experimental results gotten.

Huffman Algorithm Valuation Using Residue Number System

The Huffman algorithm is a widely used method for lossless data compression, which assigns variable-length codes to characters based on their frequency of occurrence in the input data. However, the traditional implementation of Huffman coding using binary arithmetic can be computationally intensive, particularly for large data sets. In recent years, the Residue Number System (RNS) has emerged as a promising alternative to binary arithmetic for certain types of computations, due to its potential for parallel processing and reduced hardware complexity. This paper evaluates the use of RNS as a basis for implementing the Huffman algorithm, comparing its performance with the traditional binary approach. The results demonstrate that RNS-based Huffman coding can achieve comparable or superior compression ratios, while reducing the computational requirements and potentially enabling faster compression and decompression. The study also highlights the importance of choosing appropriate RNS moduli and operands to optimize performance. Overall, the evaluation suggests that RNS can be a viable and efficient alternative to binary arithmetic for implementing the Huffman algorithm, particularly in applications with high computational demands or limited hardware resources. However, further research is needed to explore the potential benefits and limitations of RNS in other areas of data compression and signal processing.

Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

Using ChatGPT to produce code for a typical college‐level assignment

AbstractIn this article, I record experiments with ChatGPT to write code for AVL trees, a standard and reasonable sized assignment typically found during the second year in a CS curriculum. I show that it is cumbersome to elicit code that satisfies standard style guidelines. I conclude that a good textbook may be a more efficient way to develop code.

DNA Computing-Based Multi-Source Data Storage Model in Digital Twins

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT) . Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC) . The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

Analysis of Research Trends Towards Types of Code Clone Detection Techniques

Objective: The key research objectives of this study are: (1.) To compare and contrast the research trend towards the tree, token, text, metric, and graph-based code clone detection techniques; (2.) To study the distribution of metric-based code clone detection techniques on various online repositories; (3.) To make a statistical analysis of the hybrid techniques available for clone detection. The overall objective is to investigate the research trends of code clone detection approaches. Methods: Various repositories like google scholar, IEEE, and ELSEVIER Digital Libraries were systematically examined to attain the results in terms of research articles published in various places like conferences, journals, etc. followed by the inclusion and exclusion criteria. Findings: (1.) The findings related to objective 1 depicted that 50% of total clone detection techniques are tree and graph-based Code Clone Detection techniques followed by 20% of text-based and 30% of token-based code-clone detection techniques (2.) The findings related to the second objective depicted that an equal percentage of 46% of research work related to metric-based code clone detection techniques has been published in journals and conferences. (3.) The findings related to the third objective showed that 43% of hybrid code clone detection techniques are based on machine learning techniques, 24% are based on neural networks, and 18% of techniques are data mining based followed by 15% nature inspired based algorithms. Novelty: The study conducted is novel in identifying and exploring those potential code clone detection techniques that are underutilized and least explored. The result of research questions will assist researchers to draw inferences regarding usage, application, research trends, future needs, and research directions. Keywords: Code Clones; Clone Detection Techniques; Metric Based Clone Detection; Types of Clone Detection Techniques; Software Clones

Adaptive Reinforcement learning with Dij-Huff Method to Secure Optimal Route in Smart Healthcare System

The Wireless Sensor Network (WSN) is a multi-hop wireless network that contains multiple sensor nodes agreed in a self-organized mode. The significant advancement in the healthcare, the security of the medical data became huge disputes for healthcare services. Intrusion Detection System increasingly demands automatic and intelligent intrusion detection approaches to handle threats caused by a growing number of attackers in the WSN environment. Reinforcement Learning is a fundamental approach to improving routing efficiency. This approach introduces Adaptive Reinforcement learning with Dij-Huff Method (ARDM) for secure optimal route in WSN. Adaptive Reinforcement Learning (ARL) is a machine learning technique that selects the best forwarder node. The node energy, node Received Signal Strength, and node delay parameters determine the forwarder nodes in the WSN. Furthermore, the Dij-Huff Procedure (DHP) is a mixture of Dijkstra’s algorithm and Huffman coding. Dijkstra’s algorithm is applied to discover the sensor nodes with the highest energy and the best possible distance route. Huffman coding computes the binary hop count to offer each hop security. The simulation results and their comparison with conventional protocol demonstrate that the proposed scheme has a better detection ratio, a better throughput, and increased energy efficiency.

A Three-Dimensional (3D) Space Permutation and Diffusion Technique for Chaotic Image Encryption Using Merkel Tree and DNA Code

A Three-Dimensional (3D) Space Permutation and Diffusion Technique for Chaotic Image Encryption Using Merkel Tree and DNA Code

LECast: A Low-Energy-Consumption Broadcast Protocol for UAV Blockchain Networks

With the continuous development of communication technology, drones are playing an important role in many fields, such as power transmission line inspection and agricultural pesticide spraying. In order to protect the data privacy and communication security of drones, many experts are considering blockchain as its enabling technology. However, due to their small size and limited power storage, drones cannot support energy-intensive blockchain applications. In addition, the future 6G communications need to implement an important key performance indicator, namely extremely low-power communications (ELPCs). As a consequence, research into green blockchain is becoming more and more popular. The broadcast of the blockchain is one of the most energy-intensive parts because it entails flooding and there are a lot of unnecessary communication processes. Therefore, in order to make blockchain more suitable for ELPC requirements in 6G communications and unmanned aerial vehicle (UAV) networks, we took the blockchain broadcast as an improvement candidate and designed LECast, a low-energy-consumption protocol. LECast first analyzes the energy consumption model of the communication between two drones and constructs the shortest-path broadcast tree (SPB Tree) for the UAV networks to minimize energy consumption. Meanwhile, to make the sending drone address the receiving drone in a more convenient way, we proposed an extended Huffman coding (EHC) scheme to name the drones. Furthermore, the other issues with the broadcast tree are reliability and security. When a channel fails, subsequent drones cannot smoothly receive the transaction or block data. As a result, we introduced multichannel transmission with splitting data (MTSD); that is, the transaction or block data are divided into segments and transmitted in parallel multiple times over multiple channels. Finally, through the analysis and simulation of LECast in terms of energy consumption, latency, throughput, reliability, security, and coverage rate, the advantages of LECast were confirmed, which could meet the requirements of ELPCs and be well applied to UAV networks.

KryptosChain—A Blockchain-Inspired, AI-Combined, DNA-Encrypted Secure Information Exchange Scheme

Today’s digital world necessitates the adoption of encryption techniques to ensure secure peer-to-peer communication. The sole purpose of this paper is to conglomerate the fundamentals of Blockchain, AI (Artificial Intelligence) and DNA (Deoxyribonucleic Acid) encryption into one proposed scheme, KryptosChain, which is capable of providing a secure information exchange between a sender and his intended receiver. The scheme firstly suggests a DNA-based Huffman coding scheme, which alternatively allocates purines—Adenine (A) and Guanine (G), and pyrimidines—Thymine (T) and Cytosine (C) values, while following the complementary rule to higher and lower branches of the resultant Huffman tree. Inculcation of DNA concepts makes the Huffman coding scheme eight times stronger than the traditional counterpart based on binary—0 and 1 values. After the ciphertext is obtained, the proposed methodology next provides a Blockchain-inspired message exchange scheme that achieves all the principles of security and proves to be immune to common cryptographic attacks even without the deployment of any smart contract, or possessing any cryptocurrency or arriving at any consensus. Lastly, different classifiers were engaged to check the intrusion detection capability of KryptosChain on the NSL-KDD dataset and AI fundamentals. The detailed analysis of the proposed KryptosChain validates its capacity to fulfill its security goals and stands immune to cryptographic attacks. The intrusion possibility curbing concludes that the J84 classifier provides the highest accuracy of 95.84% among several others as discussed in the paper.

Algorithmic counting of nonequivalent compact Huffman codes

It is known that the following five counting problems lead to the same integer sequence ft(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${f_t}({n})$$\\end{document}: the number of nonequivalent compact Huffman codes of length n over an alphabet of t letters,the number of “nonequivalent” complete rooted t-ary trees (level-greedy trees) with n leaves,the number of “proper” words (in the sense of Even and Lempel),the number of bounded degree sequences (in the sense of Komlós, Moser, and Nemetz), andthe number of ways of writing 1=1tx1+⋯+1txn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} 1= \\frac{1}{t^{x_1}}+ \\dots + \\frac{1}{t^{x_n}} \\end{aligned}$$\\end{document} with integers 0≤x1≤x2≤⋯≤xn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0 \\le x_1 \\le x_2 \\le \\dots \\le x_n$$\\end{document}.In this work, we show that one can compute this sequence for alln<N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n<N$$\\end{document} with essentially one power series division. In total we need at most N1+ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{1+\\varepsilon }$$\\end{document} additions and multiplications of integers of cN bits (for a positive constant c<1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c<1$$\\end{document} depending on t only) or N2+ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{2+\\varepsilon }$$\\end{document} bit operations, respectively, for any ε>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon >0$$\\end{document}. This improves an earlier bound by Even and Lempel who needed O(N3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${O}({{N^3}})$$\\end{document} operations in the integer ring or O(N4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O({N^4})$$\\end{document} bit operations, respectively.

A security model to protect the isolation of medical data in the cloud using hybrid cryptography

A cloud computing platform delivers a cost-efficient path for cloud users to store and access data privately over remote storage via an Internet connection. Medical data is stored in the cloud since it is scalable, secure, reliable, provides ubiquitous access, and is highly available. It is required to be isolated in terms of physical, network, and operational for protecting data from internal threats or external cyber-attacks. It can be achievable through hybrid cryptography in which symmetric, as well as asymmetric cryptosystem, is being utilized. To assure the confidentiality and integrity of medical data, an improved Robust S-box-based Advanced Encryption Standard (IRS-AES) is proposed with Runge-Kutta Optimization (RKO) algorithm. RKO generates an enhanced and secured RS-box through the computation of the Mackey-Glass equation. Initially, the medical data are compressed using Improved Huffman Coding (IHC). The Deoxyribonucleic Acid (DNA)-based Modified Elliptic Curve Cryptography (MECC) algorithm is introduced for key generation, and the best key is selected with Bald Eagle Search optimization (BES) algorithm. Finally, the medical data are encrypted IRS-AES algorithm and stored in the cloud. The proposed RS-box security strength is evaluated using non-linearity, Strict Avalanche Criterion (SAC), Differential Probability (DP), Bit Independence Criterion (BIC), and Linear Probability (LP) parameters. The efficiency of the proposed algorithm is evaluated with image, audio, and video dataset. The evaluation metrics such as communication overhead, file upload time, computational cost, Mean Square Error (MSE), encryption and decryption time, Peak Signal-to-Noise Ratio (PSNR), key generation time, and Signal-to-Noise Ratio (SNR) are used for validating the proposed approach. The performance of the proposed approach is enhanced with the communication overhead of 11.51 for the image dataset. MSE and PSNR obtained are 16.2 and 8.465 for an audio dataset, 75.21 and 3.5 for the video dataset.

Lightweight compression with encryption based on asymmetric numeral systems

Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses Asymmetric Numeral Systems -- an innovative approach to entropy coding which can be used for compression with encryption. It provides compression ratio comparable with arithmetic coding at similar speed as Huffman coding, hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the Asymmetric Numeral System makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing additional encryption layer if reaching a satisfactory protection level.

Dynamical features of rule sequences of one dimensional cellular automata with two states and three neighbors which realize the description of digital sound data

In the present paper, we investigate the dynamical features of the rule sequence at each time step which realize the description of digital sound data. From previous research, we have performed the description with the limited number of 256 rules of three-rules sets of one dimensional cellular automata with two states and three neighbors referred as to 1-2-3 CA hereafter for various digital sounds and Huffman coding, and have successfully achieved a fully compressed description of the target sound data without reproducing the error. In order to investigate the dynamical features of the rule sequences at each time step, we perform numerical Wolfram's classification method. From computer experiments, in Pronounced word data, all the rule sequences are composed with Wolfram's class1, 2 and 4, whereas in musical data, all the rule sequences are composed with Wolfram's class2 and 4. Thus, we have succeed to classify the rule sequences among the genres of data.