Data sharing for research and industrial applications faces significant challenges due to privacy constraints and regulatory requirements, driving the need for high-quality synthetic alternatives.
Recent advances in synthetic data generation have demonstrated considerable success for single-table datasets, with emerging research extending these capabilities to multi-table relational scenarios.
While transformer and diffusion architectures achieve state-of-the-art performance in single-table generation, a notable performance gap emerges when applied to relational data, where diffusion approaches consistently outperform transformer-based methods.

This thesis examines the factors contributing to this performance difference, conducting an evaluation using multiple baselines across both single and relational tabular datasets, with REaLTabformer and ClavaDDPM as state-of-the-art transformer- and diffusion-based approaches, respectively.

Our investigation reveals that the performance can mainly be attributed to the inadequate processing of contextual relationships and suboptimal strategies for representing inter-table dependencies in transformer-based models.
To close this gap, we introduce two changes for transformer-based models: layer sharing to enhance parameter utilization and contextual encoding to better preserve the relational structure.
These changes provide insight into the key design principles behind effective synthetic relational data generation using transformer-based models, particularly the need for architectures that account for context and facilitate practical knowledge transfer.
The proposed methods result in substantial performance improvements, with a 1.52-fold improvement in Logistic Detection and a 1.94-fold reduction in the Discriminator Measure metric.

Many fields rely on scarce and sensitive time series data, such as patient health records. Privacy regulations often make sharing such data challenging, slowing research progress. Synthetic time series offer a potential solution by replicating statistical characteristics of real data without revealing private information. Yet, they introduce new risks, as synthetic data may be mistaken for real. Watermarking can mitigate this by embedding a machine-detectable signal that preserves data quality. For such methods to be effective, watermarks must be robust to removal attempts. Existing research lacks direct comparisons of generative models for time series synthesis and watermarking. Furthermore, they only evaluate watermark robustness against time-domain attacks. Attacks in other domains, such as the frequency domain, remain unexplored. In order to address these gaps, this thesis investigates three key questions. First, which generative models are best suited for time series synthesis. Second, whether latent diffusion models (LDMs) can support watermarking. Lastly, how robust existing diffusion watermarks are against adversarial attacks.

A comparative study between GPT-based models and diffusion models showed that diffusion models produce synthetic data of higher quality. LDMs were then evaluated as a potential alternative. Their reliance on a variational autoencoder led to low quality outputs. Hence, standard diffusion models were elected as the superior watermarking candidate. Finally, we introduced an extended set of time-, frequency-, and time-frequency domain attacks to asses watermark robustness. TimeWak emerged as the most robust watermark. However, our extended attack suite revealed new vulnerabilities in all watermarks, highlighting the importance of comprehensive robustness evaluations.

Data in the form of tables is commonly used in the scientific and research industry, as it provides a compact, easy-to-understand and logical way of storing data. The advancement of diffusion models has significantly improved the quality of generated tabular data, but it also poses risks of misappropriation and copyright concerns. Thus, there is a need to control and monitor the data generated by diffusion models, to enable harm mitigation and protect intellectual property. This paper addresses the necessity for robust watermarking techniques specifically designed for tabular data generated by diffusion models. We propose Ellipse, a generalization of the Tree-Ring watermarking method, originally developed for square-shaped images, to handle rectangular shaped tables. We change the shape of the watermark from a circle---fit for square-shaped images---to an oval---fit for datasets of rectangle shape.
Through comprehensive experiments on four real world datasets (Abalone, Adult, Default, and Diabetes), we demonstrate that the adapted watermarking technique has a negligible drop of 3.5% in data quality, measured through correlations between real and synthetic distributions, performance of downstream machine learning tasks, and discriminability between the real and synthetic data. This is a better result than the 12.46% drop in data quality offered by having a circle mask. Ellipse introduces a non-significant average drop of 0.4% in detection efficiency compared to having a circle mask. Our implementation also offers resilience against value skewing and deletion attacks on the rows and columns of the dataset. When exposed to attacks, Ellipse has a higher Area Under the Curve (AUC) than the circular mask of Tree-Ring by an average of 7.17%. The code for Ellipse is publicly available at https://github.com/6toma/ellipse-watermark.

Tabular data is one of the most common forms of data in the industry and science. Recent research on synthetic data generation employs auto-regressive generative large language models (LLMs) to create highly realistic tabular data samples. With the increasing use of LLMs, there is a need to govern the data generated by these models, for instance, by watermarking the model output. While the state-of-the-art Soft Red List watermarking framework has shown impressive results on standard language models, it can not be seamlessly applied to models fine-tuned for generating tabular data due to i) column permutation and ii) the task’s nature of generating low entropy sequences. We propose Tabular Red GrEen LiST (T-REST), an adaptation of the Soft Red List watermarking algorithm on tabular LLMs that is agnostic to column permutation and improves detection efficiency by employing a weighted count method that favors columns with higher entropy. Our experiments on 4 real-world datasets demonstrate that T-REST introduces a nonsignificant drop of 3% in the synthetic data quality compared to the non-watermarked data, using the resemblance and downstream machine learning efficiency metrics, while achieving high detection accuracy with AUROC of over 0.98. T-REST is insusceptible to any column or row permutation and is robust against post-editing attacks on categorical columns by maintaining a True Positive Rate (TPR) of over 0.85 when 50% of categorical values are modified.

The advent of pretrained probabilistic time series foundation models has significantly advanced the field of time series forecasting. Despite these models’ growing popularity, the application of watermarking techniques to them remains underexplored. This paper addresses this research gap by benchmarking several widely used watermarking methods to time series models and by introducing a novel watermarking technique named HTW (Heads Tails Watermark). Unlike traditional probabilistic watermarking approaches, HTW uses a pseudo-random function to directly embed a signal into the numeric structure of the series, thereby greatly enhancing its robustness against potential attacks. Comprehensive experiments and evaluations reveal that on average, HTW retains 98.4% prediction accuracy, significantly outperforming other conventional LLM watermarks. Furthermore, HTW demonstrates robust performance with an average z-score of 5.28 across various datasets and attack scenarios for a series length of 48. These findings establish HTW as a superior alternative for securing pretrained probabilistic time series foundation models

In the expanding field of generative artificial intelligence, the integration of robust watermarking technologies is essential to protect intellectual property and maintain content authenticity. Traditionally, watermarking techniques have been developed primarily for rich information media such as images and audio. However, these methods have not been adequately adapted for graph-based data, particularly on molecular graphs. Latent 3D graph diffusion(LDM-3DG) is an ascendant approach in the molecular graph generation field. This model effectively manages the complexities of molecular structures, preserving essential symmetries and topological features. To protect this sophisticated new technology, we adapt the Gaussian Shading, a proven performance lossless watermarking technique, to the latent graph diffusion domain. Our adaptation simplifies the watermark diffusion process through duplication and padding, making it adaptable and suitable for various message types.
We conduct several experiments using the LDM-3DG model on publicly available datasets QM9 and Drugs, to assess the robustness and effectiveness of our technique. Our results demonstrate that the watermarked molecules maintain statistical parity in 9 out of 10 performance metrics compared to the original. Moreover, they exhibit a 100\% detection rate and a 99\% extraction rate in a 2D decoded pipeline, while also showing robustness against post-editing attacks.

In many scientific fields, time series data is essen- tial, yet maintaining the integrity and legitimacy of such data is still difficult. Traditional watermarking techniques have mainly been used for multimedia. Although approaches for watermarking non-media data have been developed recently, there is still a big gap in the development of reliable and undetectable watermarking methods for time series diffusion models. We suggest a novel modification of the tree ring watermarking method for the 2D time series model LDCast, which is intended for precipitation prediction.
Through the incorporation of watermarks into the model’s process, we guarantee resilience and undetectability. Our approach preserves the LDCast model’s predicted accuracy while still being able to verifying the origins of the data. We confirm the efficacy of our method through comprehensive evaluation, underscoring its potential to improve the se- curity and integrity of time series forecasting models.