Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital watermarking provides additional protection by embedding an invisible watermark within the data that requires protection. However, little work has been taken to analyze and verify if existing digital text watermarking methods are secure and undetectable by large language models. In this paper, we investigate the security-related area of watermarking and machine learning models for text data. In a controlled testbed of three experiments, ten existing Unicode text watermarking methods were implemented and analyzed across six large language models: GPT-5, GPT-4o, Teuken 7B, Llama 3.3, Claude Sonnet 4, and Gemini 2.5 Pro. The findings of our experiments indicate that, especially the latest reasoning models, can detect a watermarked text. Nevertheless, all models fail to extract the watermark unless implementation details in the form of source code are provided. We discuss the implications for security researchers and practitioners and outline future research opportunities to address security concerns.

Large language models (LLMs) have gained significant popularity in recent years. Differentiating between a text written by a human and one generated by an LLM has become almost impossible. Information-hiding techniques such as digital watermarking or steganography can help by embedding information inside text in a form that is unlikely to be noticed. However, existing techniques, such as linguistic-based or format-based methods, change the semantics or cannot be applied to pure, unformatted text. In this paper, we introduce a novel method for information hiding called Innamark, which can conceal any byte-encoded sequence within a sufficiently long cover text. This method is implemented as a multi-platform library using the Kotlin programming language, which is accompanied by a command-line tool and a web interface. By substituting conventional whitespace characters with visually similar Unicode whitespace characters, our proposed scheme preserves the semantics of the cover text without changing the number of characters. Furthermore, we propose a specified structure for secret messages that enables configurable compression, encryption, hashing, and error correction. An experimental benchmark comparison on a dataset of 1 000 000 Wikipedia articles compares ten algorithms. The results demonstrate the robustness of our proposed Innamark method in various applications and the imperceptibility of its watermarks to humans. We discuss the limits to the embedding capacity and robustness of the algorithm and how these could be addressed in future work.

Data spaces have emerged as a paradigm for maintaining data sovereignty and interoperability in data sharing among various stakeholders. There is an increasing interest and progress in research and practice whereby currently used implementations only enable data sovereignty within their trusted environments. Digital watermarking, a key concept in the research domain of information hiding, addresses similar principles of securing data ownership, while its integration within data spaces remains unexplored. This paper combines both domains by presenting the first integrated digital watermarking solution in a data space. Guided by design science research, we designed and developed two extensions for the connector of the Eclipse Dataspace Components in the Java programming language to validate the solution's practicality. The resulting artifact shows a robust data sovereignty enhancement, paving the way for more protection and control in future data spaces.

The possibility of hiding information inside a digital medium is often referred to as watermarking or steganography. Since various solutions for image, video, and audio files exist, keeping control over text is challenging due to its limited possibilities. In this paper, we present a new digital text watermarking algorithm to hide a byte-encoded sequence inside an unformatted text. By substituting conventional whitespaces with a set of five similar-looking Unicode spaces, the cover text's structure and length stay untouched while remaining imperceptible to humans. We propose a software design and proof-of-concept multiplatform implementation with a downstream experimental evaluation for robustness, capacity, and visibility. Our findings indicate a stronger concealment and application robustness with limited embedding capacity compared to existing solutions utilizing zero-width spaces.

Data has become a strategic asset for societal prosperity and economic competitiveness. There has long been an academic consensus that the value of data unfolds during its use. Consequently, many stakeholders have called for expanding the use and reuse of data, including the public and open variety, as well as that from private data providers. However, citizens and organizations want self-determination over their data use, that is, data sovereignty. This fundamentals paper applies a literature review to conceptualize the term in Information Systems (IS) research by summarizing current findings and definitions to add further structure to the field. It contributes to the current research streams by introducing a core conceptual model consisting of seven interacting core aspects, involving trust between data providers and consumers for data assets, supported by data infrastructure and contractual agreements on all data lifecycle stages. We evaluate and discuss this conceptual model through recent field examples and provide an overview of future research opportunities.

Digital Twins have evolved as a concept describing digital representations of physical assets. They can be used to facilitate simulations, monitoring, or optimization of product lifecycles. Considering the concept of a Circular Economy, which entails several lifecycles of, e.g., vehicles, their components, and materials, it is important to investigate how the respective Digital Twins are managed over the lifecycle of their physical assets. This publication presents and compares three approaches for managing Digital Twins in industrial use cases. The analysis considers aspects such as updates, data ownership, and data sovereignty. The results based on the research project Catena-X

Digital technology significantly impacts our everyday social lives and how we conduct business. This development results in abundant new data generated by people and organizations. Subsequently, future technological instruments must ensure data sovereignty that empowers individuals to maintain control over their data. However, data sovereignty is still blurry and conceptually overlaps with similar terminologies, such as digital and technological sovereignty. From an Information Systems (IS) point of view, delimiting data sovereignty from digital and technological sovereignty is crucial, creating a uniform understanding, especially for data ecosystems. Our study contributes to sharpening data sovereignty with a systematic literature review of 81 articles. It concludes that data sovereignty mainly drives IS activities by protecting data assets on individual and organizational levels. In contrast, digital sovereignty is shaped by digital expertise and interoperability, while technological sovereignty is the broadest concept with regulations and relations on an international level.

Data sovereignty, the possibility to keep control over data, is gaining increasing attention in both research and industry. Due to complex supply chains and a strong trend toward digitization, digital assets are essential to be fast and competitive. As a result, companies need to share data while retaining control over it to prevent unwanted leaks of sensitive data. However, implementing effective data governance, access, and usage control mechanisms can be challenging, especially in cross-company data sharing networks and ecosystems like dataspaces. In this paper, we examine the industrial landscape and interview eleven experts from software providers and producing organizations to identify their requirements and challenges of existing data sovereign solutions. Based on Grounded Theory and semi-structured interviews, we explore the motivations and issues behind data sharing from an Information Systems and Software Engineering point of view. The findings include current industrial contexts, use cases, and solutions with data sovereignty’s technical and non-technical implementations. Seven requirements and thirteen challenges were observed throughout a qualitative analysis. Clustered by organizational, technical, personal, and emotional viewpoints, they are discussed with initial approaches for mitigation. The results identify current practical needs and will enable the design of future data sovereignty solutions in theory and different practical domains.

Although digitization is advancing rapidly, a large amount of data processed by companies is in printed format. Technologies such as Optical Character Recognition (OCR) support the transformation of printed text into machine-readable content. However, OCR struggles when data on documents is highly unstructured and includes non-text objects. This, e.g., applies to documents such as medical prescriptions. Leveraging Design Science Research (DSR), we propose a flexible processing pipeline that can deal with character recognition on the one hand and object detection on the other hand. To do so, we derive Design Requirements (DR) in cooperation with a practitioner doing prescription billing in the healthcare domain. We then developed a prototype blueprint that is applicable to similar problem formulations. Overall, we contribute to research and practice in multiple ways. First, we provide evidence for selected OCR methods provided by previous research. Second, we design a machine-learning-based digitization pipeline for printed documents containing both text and non-text objects in the context of medical prescriptions. Third, we derive a nascent design pattern for this type of document digitization. These patterns are the foundation for further research and can support the development of innovative information systems leading to more efficient decision making and thus to economic resource usage.

Data sovereignty is gaining increasing importance as the frequency and sensitivity of data exchange between companies and nations increase. Existing approaches ensuring sovereign data exchange in business ecosystems, like the International Data Spaces (IDS) initiative, neglect limitations of hardware resource restrictions. Therefore, we examine real-time sovereign data exchange in cloud-connected Internet of Things (IoT) devices. Two lightweight communication schemes based on request/response and publish/subscribe are proposed and implemented following the IDS guidelines. For evaluation, we use a simulated test-bed based on an Automated Guided Vehicle (AGV) use case. We examine the results based on exchanged IDS messages and CPU usage on the consumer side represented by the AGVs as IoT devices. Results show benefits in the publish/subscribe version in longer operation times, allowing to enter low-power mode, while request/response performs better on limited CPU resources or short operations.