Hiding Data Using Efficient Combination of RSA Cryptography, and Compression Steganography Techniques

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.

With the resolution enhancement techniques such as OPC (Optical Proximity Correction) and SRAF (Sub-Resolution Assist Features), the size of layout data have grown significantly. It is quite common now to find layout files that are tens of GBs in size. Unlike GDSII which can store data hierarchically, mask data formats such as MEBES are essentially flat and more voluminous. Moreover, polygonal data present in layout data files is fractured, thereby increasing the data volume before getting stored in MEBES data format. This results in huge MEBES files. As per the ITRS roadmap of 2005, for a 45nm half-pitch node that is expected to be in use by 2010, the mask data volume for a single layer is expected to reach up to 825 GB. Storing and transferring such large mask data are issues for which the mask industry needs solutions. Historically, MEBES is the most prevalent EB format in the industry. Moreover, in many Mask Data Preparation (MDP) flows, the MEBES format is being used as the de-facto standard for specifying the fractured EB data even though the final target EB machine might be different. In this paper we present techniques for lossless reversible compression of MEBES data, i.e., when the compressed file is decompressed, the generated uncompressed file matches the original MEBES file bit- by-bit. By applying these compression techniques a compression ratio of 5X to 15X can be obtained. In practice, compressing MEBES files is usually a one-time task, but decompression of compressed files is expected to be done multiple times as every time a compressed MEBES file needs processing, it has to be decompressed. MEBES is essentially an efficient data format and the geometries are stored compactly. As a result the compression/decompression techniques described in this paper are quite computation intensive in order to achieve higher compression ratio. This in turn leads to higher CPU time for compression/decompression compared to generic compressors such as gzip. However, as the format-specific compressors produce higher compression ratios, the disk I/O time for compression and decompression is expected to be less compared to the generic compressors such as gzip and gunzip. In spite of this, the format-specific decompressor is usually 3-5X times slower than the generic decompressor such as gunzip. Since decompression is expected to be done more frequently as compared to compression, speeding up decompression is highly desirable. We present a compression technique for MEBES data which enables multiple process threads to decompress the compressed MEBES data. With the multi-core multiprocessor machines becoming quite inexpensive and common, the multi-threaded decompression is expected to perform close to the disk I/O time on such machines. The paper details out the techniques, experimental results in terms of comparative compression ratios, compression and decompression speed using single threads and multiple threads. The possibility of getting higher compression ratios as well as higher or comparable decompression speed makes it more practical to use format specific reversible compression schemes rather than using generic compressors. Even though, the paper focuses on compression and decompression of MEBES, it can be easily extended to the compression of GDSII [1].

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

<div class="section abstract"> <div class="htmlview paragraph">This recommended practice aims to establish a common output format of crash-related data recorded and stored within certain electronic components currently installed in many light-duty vehicles. This document pertains only to the post-download format of such data and is not intended to standardize the format of the data stored within any on-board storage unit, or to standardize the method of data recording, storing, or extraction.</div> <div class="htmlview paragraph">Historically, crash data recording technology in light-duty vehicles has developed and evolved based on differing technical needs of manufacturers and their customers without industry standards or government regulation. As a result, wide variations currently exist among vehicle manufacturers regarding the scope and extent of recorded data. This document is intended to be a compilation of data elements that various manufacturers are currently recording, as well as those elements reasonably predicted to be recorded in the foreseeable future, and establishes a common output format of those data elements.</div> <div class="htmlview paragraph">The scope of this document is deliberately restricted to providing output data formatting guidelines, only. It is not intended to standardize or mandate the recording of any specific data elements or to specify a minimum data set. This restriction in the scope is to avoid imposing constraints on the type of data recorded, the method of gathering and storing data, and the pre-and/or post-processing of data that may be used by various vehicle/device manufacturers as a matter of proprietary design. The committee recognized that, given the lack of societal consensus regarding the acceptability of on-board data recording devices, and has therefore avoided specific recommendations concerning whether event data recorders should be equipped on motor vehicles, and whether, when implemented, a particular type, quantity, and manner of data collection should be used.</div> <div class="htmlview paragraph">Readers of this series of documents are advised to examine the validity and accuracy of VEDI event data in conjunction with the results from the traditional accident reconstruction practice. Since VEDI event data are acquired only from the on-board electronics systems, they may have not accurately capture the actual dynamics of the vehicle during the accident, or may not contain the full range or length of data during the event.</div> <div class="htmlview paragraph">This document is one of a multi-volume J1698 series of documents and provides general concepts and common definitions of event data recording. J1698-1 provides definitions of event data and J1698-2 provides data extraction protocols.</div> </div>

Accessing data by an unauthorized person often happens these days. Addressing this case, the techniques of cryptography and steganography was necessary. The science of cryptography use randomizes/disguise the data, whereas the science of steganography to hide data in a way. The cryptographic algorithms to be used is the Rivest Shamir Adleman (RSA) which utilizes two prime numbers to generate a public key and private key. To further enhance the security of message, the algorithm has combined with the First of File (FoF) as steganography techniques. The technique First of File (FoF) works by inserting a message at the beginning of the file image horizontally to produce a stego image that will extract to get the previous ciphertext. Then the ciphertext decrypted to be returned to the original message. By using FoF, in the cover image with large pixel size, the insertion process with FoF is entirely invisible, so the stego image and original image cannot be distinguished, whereas in small pixels there is a black line above the image. A combination of RSA and the FOF will increase data security because when a message has scrambled into a ciphertext and the ciphertext will be inserted into the image file.

For many computerized applications, data compression is a standard requirement. In order to minimize the capacity needed for that data, it decreases the redundancy in data representation and thus therefore reduces the connectivity cost by efficiently utilizing the available bandwidth. There are a range of algorithms for data compression that are used to compress various formats of data. There are also sets of different compression algorithms for a single data form, which use different approaches. This paper assesses lossless algorithms for compression of data and compares their performance. To assess the performance of compressing text data, images, audio, a set of selected algorithms are used. Experimental findings and comparisons of algorithms for lossless compression using methods for compression. It gives contrasting conclusions on the size and time ratios toward existing research. Different information that can be compressed not only text but it can be Audio, pictures and video information. In this article, there are variety of various forms of lossless compression algorithms discussed.KeywordsData compressionLossless data compressionText compressionImage compressionAudio compression

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

The increasing test data volume is considered as a biggest challenge in circuit under test. This challenge leads to higher computational complexity associated with the testing circuits. To reduce the testing time and addresses the problem related to high overhead, high fault coverage andincreased power dissipationmethod is used in system-on-chip (SoC). A new test compression method called divide and compress (D&C) lossless compression coder with Discrete Cosine Transformis used to enhance the compression capability. This framework of D&C lossless compression coder combines the lossless and lossy compression methods. The Discrete Cosine Transform (DCT) manipulates each data bit inside file and this reduces the file size without any data loss after decoding to lossless compression.Depending on the data characteristic, the compression of data is done without any loss in data. Further, the decompression is attained using advanced Finite State Machine (FSM) to reduce overhead. The experimental results on large benchmark circuits like ITC’99 and ISCAS’99 proves that the proposed D&C lossless compression attains reduced testing time and better compression ratio. Further, itlays only soft burden on the hardware.

Chapter 1 - Introduction

Steganography, the technology of protecting a secret message by embedding it inside a cover image, continues to be investigated and enhanced as an alternative data protection method. This paper deals with hiding multimedia files in true color RGB cover images with an emphasis on reducing the cover size, increasing hiding capacity and enhancing security of the hidden data. A proposed model (DuoHide) is presented in which a secret multimedia file, regardless of its type, is processed without un-compression, and divided between two cover images of equal size and dimensions. The multimedia file is read as a stream of bytes and split vertically into two parts, one part contains the least significant half-bytes, and the other part contains the most significant half-bytes. The two parts are hidden inside two uncompressed RGB cover images using a least significant 4-bit replacement technique. The resulting dual stego images are expected to be sent separately, through different channels, to avoid capture of both stego files by an adversary. Extraction of the secret file is achieved through merging LSB half-bytes from the two stego files. The extracted file is identical in content and structure with the original secret file. The implemented DuoHide system was evaluated using a set of public multimedia files, images, audios, and videos, of various sizes. The secret file sizes ranged from 5% to about 100% of the cover image's size. The experimental results showed that even at the highest embedding ratio, which is based on the secret-to-cover ratio, there were no perceptible visual differences between cover and stego images. The PNSR value was calculated as PSNR1, for cover1 and stego1, and PSNR2 for cover2 and stego2. The lowest PSNR value was around 31 dB for the highest embedding ratio, which is considered acceptable concerning statistical imperceptibility. The PSNR value increased as the embedding ratio decreased, reaching around 65 Decibel (dB) for the case of 5% secret-to-cover ratio. The integrity of the extracted secret file was verified through a bitwise comparison between original and extracted files, which showed zero differences. The DuoHide model is expected to provide better security for the hidden file, in case an attacker manages to capture one of the stego images and recover the hidden content because the attacker will only get an incomprehensible set of half-byte bits. An additional advantage of using a pair of stego files is that of reducing stego file size by 50%, to avoid problems and limitations of transmitting large files, especially that multimedia files are often large, and they cannot be compressed because they are already compressed. Security of the DuoHide system can further be improved by randomizing storage locations within the two stego images.

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

he rapid growth of digital technology has led to an exponential increase in data volume, creating challenges in data storage and transmission efficiency. Data compression plays a critical role in addressing these issues, particularly for text files, which are among the most commonly used data formats. This study aims to perform a comparative analysis between the Ternary Comma Code (TCC) algorithm and the Fibonacci algorithm in text file compression. Experiments were conducted using various text files of different sizes, with key evaluation metrics including compression ratio, compression time, and decompression efficiency. The results indicate that each algorithm performs better under different conditions: TCC provides a higher compression ratio, while Fibonacci achieves faster processing times for small to medium-sized files. This research contributes to selecting the most suitable compression algorithm based on the specific needs and characteristics of the text data being compressed.

Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment.

The development of large-capacity storage media causes people to no longer have problems if they have large files. More so if the file we have is an image file. Nevertheless, sometimes the large file size feels annoying if we have to manage the storage media that we have for various data, thepurpose of this study is to compress color imagery. By using quantization method can be taken, By using data compression then the data capacity will be minimized, Utilization of quantization method will facilitate data compression (image), Image compression with kunatization method in RGB Image can reduce thesize of the compressed file, so as to save storage space(Storage).