On Information Hiding in Natural Language Systems
This work addresses privacy preservation for users of natural language-based systems, but it is incremental as it builds on existing NLS methods without introducing a new paradigm.
The paper tackles the problem of improving data security and confidentiality in natural language systems by focusing on Natural Language Steganography (NLS) methods, summarizing challenges and proposing directions to enhance steganographic text quality without providing specific numerical results.
With data privacy becoming more of a necessity than a luxury in today's digital world, research on more robust models of privacy preservation and information security is on the rise. In this paper, we take a look at Natural Language Steganography (NLS) methods, which perform information hiding in natural language systems, as a means to achieve data security as well as confidentiality. We summarize primary challenges regarding the secrecy and imperceptibility requirements of these systems and propose potential directions of improvement, specifically targeting steganographic text quality. We believe that this study will act as an appropriate framework to build more resilient models of Natural Language Steganography, working towards instilling security within natural language-based neural models.