Performance analysis on secured data method in natural language steganography

The rapid amount of exchange information that causes the expansion of the internet during the last decade has motivated that a research in this field.  Recently, steganography approaches have received an unexpected attention. Hence, the aim of this paper is to review different performance metric; covering the decoding, decrypting and extracting performance metric. The process of data decoding interprets the received hidden message into a code word. As such, data encryption is the best way to provide a secure communication. Decrypting take an encrypted text and converting it back into an original text. Data extracting is a process which is the reverse of the data embedding process. The effectiveness evaluation is mainly determined by the performance metric aspect. The intention of researchers is to improve performance metric characteristics. The evaluation success is mainly determined by the performance analysis aspect.  The objective of this paper is to present a review on the study of steganography in natural language based on the criteria of the performance analysis. The findings review will clarify the preferred performance metric aspects used. This review is hoped to help future research in evaluating the performance analysis of natural language in general and the proposed secured data revealed on natural language steganography in specific

Covert Channels Within IRC

The exploration of advanced information hiding techniques is important to understand and defend against illicit data extractions over networks. Many techniques have been developed to covertly transmit data over networks, each differing in their capabilities, methods, and levels of complexity. This research introduces a new class of information hiding techniques for use over Internet Relay Chat (IRC), called the Variable Advanced Network IRC Stealth Handler (VANISH) system. Three methods for concealing information are developed under this framework to suit the needs of an attacker. These methods are referred to as the Throughput, Stealth, and Baseline scenarios. Each is designed for a specific purpose: to maximize channel capacity, minimize shape-based detectability, or provide a baseline for comparison using established techniques applied to IRC. The effectiveness of these scenarios is empirically tested using public IRC servers in Chicago, Illinois and Amsterdam, Netherlands. The Throughput method exfiltrates covert data at nearly 800 bits per second (bps) compared to 18 bps with the Baseline method and 0.13 bps for the Stealth method. The Stealth method uses Reed-Solomon forward error correction to reduce bit errors from 3.1% to nearly 0% with minimal additional overhead. The Stealth method also successfully evades shape-based detection tests but is vulnerable to regularity-based tests

Privacy-Preserving Screen Capture: Closing the Loop for Medical Informatics Usability

As information technology permeates healthcare (particularly provider-facing systems), maximizing system effectiveness requires the ability to document and analyze tricky or troublesome usage scenarios. However, real-world medical applications are typically replete with privacy-sensitive data regarding patients, diagnoses, clinicians, and EMR user interface details; any instrumentation for screen capture (capturing and recording the scenario depicted on the screen) needs to respect these privacy constraints. Furthermore, real-world medical informatics systems are typically composed of modules from many sources, mission-critical and often closed-source; any instrumentation for screen capture cannot rely on access to structured output or software internals. In this paper, we present a solution: a system that combines keyboard video mouse (KVM) capture with automatic text redaction (and interactively selectable unredaction) to produce precise technical content that can enrich stakeholder communications and improve end-user influence on system evolution. KVM-based capture makes our system both application and operating-system independent because it eliminates software-interface dependencies on capture targets. Using a corpus of EMR screenshots, we present empirical measurements of redaction effectiveness and processing latency to demonstrate system performances. We discuss how these techniques can translate into instrumentation systems that improve real-world medical informatics deployments

Perspective Chapter: Text Watermark Analysis - Concept, Technique, and Applications

Watermarking is a modern technology in which identifying information is embedded in a data carrier. It is not easy to notice without affecting data usage. A text watermark is an approach to inserting a watermark into text documents. This is an extremely complex undertaking, especially given the scarcity of research in this area. This process has proven to be very complex, especially since there has only been a limited amount of research done in this field. Conducting an in-depth analysis, analysis, and implementation of the evaluation, is essential for its success. The overall aim of this chapter is to develop an understanding of the theory, methods, and applications of text watermarking, with a focus on procedures for defining, embedding, and extracting watermarks, as well as requirements, approaches, and linguistic implications. Detailed examination of the new classification of text watermarks is provided in this chapter as are the integration process and related issues of attacks and language applicability. Research challenges in open and forward-looking research are also explored, with emphasis on information integrity, information accessibility, originality preservation, information security, and sensitive data protection. The topics include sensing, document conversion, cryptographic applications, and language flexibility

Federated Learning for Protecting Medical Data Privacy

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access to medical data, these Deep learning techniques have yet to completely exploit medical data despite their immense potential benefits. Many data proprietors are unable to benefit from large-scale deep learning due to privacy and confidentiality concerns associated with data sharing. However, without access to sufficient data, Deep Learning will not be able to realize its maximum potential when transitioning from the research phase to clinical practice [2]. This project addresses this problem by implementing Federated Learning and Encrypted Computations on text data, such as Multi Party Computation. SyferText, a Python library for privacy-protected Natural Language Processing that leverages PySyft to conduct Federated Learning, is used in this context

A Comparative Review on Data Hiding Schemes

Data hiding is a technique used to protect confidential information.The aim of a particular data hiding scheme is to make a more secure and robust method of information exchange so that confidential and private data can be protected against attacks and illegal access. The aim of this paper is to review on different data hiding schemes, covering the decoding, decrypting and extracting schemes.This paper also highlighted three major schemes that are widely used in research and real practice.The discussion include findings on the most recent work on decryption schemes

Spatial Hypermedia as a programming environment

This thesis investigates the possibilities opened to a programmer when their programming environment not only utilises Spatial Hypermedia functionality, but embraces it as a core component. Designed and built to explore these possibilities, SpIDER (standing for Spatial Integrated Development Environment Research) is an IDE featuring not only traditional functionality such as content assist and debugging support but also multimedia integration and free-form spatial code layout. Such functionality allows programmers to visually communicate aspects of the intent and structure of their code that would be tedious—and in some cases impossible—to achieve in conventional IDEs. Drawing from literature on Spatial Memory, the design of SpIDER has been driven by the desire to improve the programming experience while also providing a flexible authoring environment for software development. The programmer’s use of Spatial Memory is promoted, in particular, by: utilising fixed sized authoring canvases; providing the capacity for landmarks; exploiting a hierarchical linking system; and having well defined occlusion and spatial stability of authored code. The key challenge in implementing SpIDER was to devise an algorithm to bridge the gap between spatially expressed source code, and the serial text forms required by compilers. This challenge was met by developing an algorithm that we have called the flow walker. We validated this algorithm through user testing to establish that participants’ interpretation of the meaning of spatially laid out code matched the flow walker’s implementation. SpIDER can be obtained at: https://sourceforge.net/projects/spatial-ide-research-spide

Creating Network Attack Priority Lists by Analyzing Email Traffic Using Predefined Profiles

Networks can be vast and complicated entities consisting of both servers and workstations that contain information sought by attackers. Searching for specific data in a large network can be a time consuming process. Vast amounts of data either passes through or is stored by various servers on the network. However, intermediate work products are often kept solely on workstations. Potential high value targets can be passively identified by comparing user email traffic against predefined profiles. This method provides a potentially smaller footprint on target systems, less human interaction, and increased efficiency of attackers. Collecting user email traffic and comparing each word in an email to a predefined profile, or a list of key words of interest to the attacker, can provide a prioritized list of systems containing the most relevant information. This research uses two experiments. The functionality experiment uses randomly generated emails and profiles, demonstrating MAPS (Merritt\u27s Adaptive Profiling System)ability to accurately identify matches. The utility experiment uses an email corpus and meaningful profiles, further demonstrating MAPS ability to accurately identify matches with non-random input. A meaningful profile is a list of words bearing a semantic relationship to a topic of interest to the attacker. Results for the functionality experiment show MAPS can parse randomly generated emails and identify matches with an accuracy of 99 percent or above. The utility experiment using an email corpus with meaningful profiles, shows slightly lower accuracies of 95 percent or above. Based upon the match results, network attack priority lists are generated. A network attack priority list is an ordered list of systems, where the potentially highest value systems exhibit the greatest fit to the profile. An attacker then uses the list when searching for target information on the network to prioritize the systems most likely to contain useful data