Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding

Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided to two main categories, i.e., modification based LS (MLS) and generation based LS (GLS). Unlike MLS that hides secret data by slightly modifying a given text without impairing the meaning of the text, GLS uses a trained language model to directly generate a text carrying secret data. A common disadvantage for MLS methods is that the embedding payload is very low, whose return is well preserving the semantic quality of the text. In contrast, GLS allows the data hider to embed a high payload, which has to pay the high price of uncontrollable semantics. In this paper, we propose a novel LS method to modify a given text by pivoting it between two different languages and embed secret data by applying a GLS-like information encoding strategy. Our purpose is to alter the expression of the given text, enabling a high payload to be embedded while keeping the semantic information unchanged. Experimental results have shown that the proposed work not only achieves a high embedding payload, but also shows superior performance in maintaining the semantic consistency and resisting linguistic steganalysis

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

An embedding traid-bit method to improve the performance of Arabic text steganography

The enormous development in the utilization of the Internet has driven by continuous improvements in the region of security. The enhanced security techniques are applied to save the intellectual property. There are numerous sorts of security mechanisms. Steganography is the art and science of concealing secret information inside a cover media without drawing any suspicion to the eavesdropper so that the secret information can only be detected by its proposed recipient. This is done along with the other steganography methods such as image, audio, video, various text steganography methods that are being presented. The text is ideal for steganography due to its ubiquity. There are many steganography methods used several languages such as English, Chines and Arabic language to embed the hidden message in the cover text. Kashida, shifting point and sharp_edges are Arabic steganography methods with high capacity. However, kashida, shifting point and sharp_edges techniques have lack of capability to embed the hidden message into the cover text. This study proposed new method called Traid-bit method by integrating three several types of methods such us kashida, shifting point and sharp_edges to evaluate the proposed method in improving the performance of embedding process. The study presents the process design of proposed method including the algorithms and the system design. The study found that the evaluation of the proposed method provides good knowledge to steganographer to improve the performance of embedding process when the Arabic text steganography method is developed

Hiding Information in Reversible English Transforms for a Blind Receiver

This paper proposes a new technique for hiding secret messages in ordinary English text. The proposed technique exploits the redundancies existing in some English language constructs. Redundancies result from the flexibility in maneuvering certain statement constituents without altering the statement meaning or correctness. For example, one can say "she went to sleep, because she was tired" or "Because she was tired, she went to sleep. " The paper provides a number of such transformations that can be applied concurrently, while keeping the overall meaning and grammar intact. The proposed data hiding technique is blind since the receiver does not keep a copy of the original uncoded text (cover). Moreover, it can hide more than three bits per statement, which is higher than that achieved in the prior work. A secret key that is a function of the various transformations used is proposed to protect the confidentiality of the hidden message. Our security analysis shows that even if the attacker knows how the transforms are employed, the secret key provides enough security to protect the confidentiality of the hidden message. Moreover, we show that the proposed transformations do not affect the inconspicuousness of the transformed statements, and thus unlikely to draw suspicion

Hiding Information in Reversible English Transforms for a Blind Receiver

This paper proposes a new technique for hiding secret messages in ordinary English text. The proposed technique exploits the redundancies existing in some English language constructs. Redundancies result from the flexibility in maneuvering certain statement constituents without altering the statement meaning or correctness. For example, one can say “she went to sleep, because she was tired” or “Because she was tired, she went to sleep.” The paper provides a number of such transformations that can be applied concurrently, while keeping the overall meaning and grammar intact. The proposed data hiding technique is blind since the receiver does not keep a copy of the original uncoded text (cover). Moreover, it can hide more than three bits per statement, which is higher than that achieved in the prior work. A secret key that is a function of the various transformations used is proposed to protect the confidentiality of the hidden message. Our security analysis shows that even if the attacker knows how the transforms are employed, the secret key provides enough security to protect the confidentiality of the hidden message. Moreover, we show that the proposed transformations do not affect the inconspicuousness of the transformed statements, and thus unlikely to draw suspicion

Building Security Protocols Against Powerful Adversaries

As our sensitive data is increasingly carried over the Internet and stored remotely, security in communications becomes a fundamental requirement. Yet, today's security practices are designed around assumptions the validity of which is being challenged. In this thesis we design new security mechanisms for certain scenarios where traditional security assumptions do not hold. First, we design secret-agreement protocols for wireless networks, where the security of the secrets does not depend on assumptions about the computational limitations of adversaries. Our protocols leverage intrinsic characteristics of the wireless to enable nodes to agree on common pairwise secrets that are secure against computationally unconstrained adversaries. Through testbed and simulation experimentation, we show that it is feasible in practice to create thousands of secret bits per second. Second, we propose a traffic anonymization scheme for wireless networks. Our protocol aims in providing anonymity in a fashion similar to Tor - yet being resilient to computationally unbounded adversaries - by exploiting the security properties of our secret-agreement. Our analysis and simulation results indicate that our scheme can offer a level of anonymity comparable to the level of anonymity that Tor does. Third, we design a lightweight data encryption protocol for protecting against computationally powerful adversaries in wireless sensor networks. Our protocol aims in increasing the inherent weak security that network coding naturally offers, at a low extra overhead. Our extensive simulation results demonstrate the additional security benefits of our approach. Finally, we present a steganographic mechanism for secret message exchange over untrustworthy messaging service providers. Our scheme masks secret messages into innocuous texts, aiming in hiding the fact that secret message exchange is taking place. Our results indicate that our schemes succeeds in communicating hidden information at non-negligible rates

Security and Privacy for the Modern World

The world is organized around technology that does not respect its users. As a precondition of participation in digital life, users cede control of their data to third-parties with murky motivations, and cannot ensure this control is not mishandled or abused. In this work, we create secure, privacy-respecting computing for the average user by giving them the tools to guarantee their data is shielded from prying eyes. We first uncover the side channels present when outsourcing scientific computation to the cloud, and address them by building a data-oblivious virtual environment capable of efficiently handling these workloads. Then, we explore stronger privacy protections for interpersonal communication through practical steganography, using it to hide sensitive messages in realistic cover distributions like English text. Finally, we discuss at-home cryptography, and leverage it to bind a user’s access to their online services and important files to a secure location, such as their smart home. This line of research represents a new model of digital life, one that is both full-featured and protected against the security and privacy threats of the modern world

Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review

Deep Neural Networks (DNNs) have led to unprecedented progress in various natural language processing (NLP) tasks. Owing to limited data and computation resources, using third-party data and models has become a new paradigm for adapting various tasks. However, research shows that it has some potential security vulnerabilities because attackers can manipulate the training process and data source. Such a way can set specific triggers, making the model exhibit expected behaviors that have little inferior influence on the model's performance for primitive tasks, called backdoor attacks. Hence, it could have dire consequences, especially considering that the backdoor attack surfaces are broad. To get a precise grasp and understanding of this problem, a systematic and comprehensive review is required to confront various security challenges from different phases and attack purposes. Additionally, there is a dearth of analysis and comparison of the various emerging backdoor countermeasures in this situation. In this paper, we conduct a timely review of backdoor attacks and countermeasures to sound the red alarm for the NLP security community. According to the affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into three categorizations: attacking pre-trained model with fine-tuning (APMF) or prompt-tuning (APMP), and attacking final model with training (AFMT), where AFMT can be subdivided into different attack aims. Thus, attacks under each categorization are combed. The countermeasures are categorized into two general classes: sample inspection and model inspection. Overall, the research on the defense side is far behind the attack side, and there is no single defense that can prevent all types of backdoor attacks. An attacker can intelligently bypass existing defenses with a more invisible attack. ......Comment: 24 pages, 4 figure

Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings

Making computers automatically find the appropriate meaning of words in context is an interesting problem that has proven to be one of the most challenging tasks in natural language processing (NLP). Widespread potential applications of a possible solution to the problem could be envisaged in several NLP tasks such as text simplification, language learning, machine translation, query expansion, information retrieval and text summarization. Ambiguity of words has always been a challenge in these applications, and the traditional endeavor to solve the problem of this ambiguity, namely doing word sense disambiguation using resources like WordNet, has been fraught with debate about the feasibility of the granularity that exists in WordNet senses. The recent trend has therefore been to move away from enforcing any given lexical resource upon automated systems from which to pick potential candidate senses,and to instead encourage them to pick and choose their own resources. Given a sentence with a target ambiguous word, an alternative solution consists of picking potential candidate substitutes for the target, filtering the list of the candidates to a much shorter list using various heuristics, and trying to match these system predictions against a human generated gold standard, with a view to ensuring that the meaning of the sentence does not change after the substitutions. This solution has manifested itself in the SemEval 2007 task of lexical substitution and the more recent SemEval 2010 task of cross-lingual lexical substitution (which I helped organize), where given an English context and a target word within that context, the systems are required to provide between one and ten appropriate substitutes (in English) or translations (in Spanish) for the target word. In this dissertation, I present a comprehensive overview of state-of-the-art research and describe new experiments to tackle the tasks of lexical substitution and cross-lingual lexical substitution. In particular I attempt to answer some research questions pertinent to the tasks, mostly focusing on completely unsupervised approaches. I present a new framework for unsupervised lexical substitution using graphs and centrality algorithms. An additional novelty in this approach is the use of directional similarity rather than the traditional, symmetric word similarity. Additionally, the thesis also explores the extension of the monolingual framework into a cross-lingual one, and examines how well this cross-lingual framework can work for the monolingual lexical substitution and cross-lingual lexical substitution tasks. A comprehensive set of comparative investigations are presented amongst supervised and unsupervised methods, several graph based methods, and the use of monolingual and multilingual information