In the recent years, several papers have implemented methods which seamlessly integrate data streams into high frame rate monitors without affecting casual viewer experience. This paper proposes an improvement to existing systems which can in theory increase the data throughput of the data stream several times. Exploiting the same characteristics of human vision as related papers, grid containing different colours is encoded into a video. Testing was conducted to determine the possible uses of this approach. It was determined that 2x or even higher increase is possible however there seems to always be a visual artifact present. The testing also revealed a need for further testing to determine the best possible configuration (amount of colours) to use as well. This testing would also increase the reliability of the data gathered as the experiment was limited by time.

Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions, and more. Consequently, an effective approach to ensuring the privacy of medical data must be easy to adopt, offer robust privacy guarantees, and minimize the reduction in data utility.

The unique nature of medical data presents distinct challenges and also opportunities. We consider various types of correlations that significantly impact privacy guarantees. However, these correlations can also be used to train a model for removing anomalies and subsequently enhancing the utility of synthetic medical data.

This thesis proposes a framework compatible with state-of-the-art approaches for differentially private dataset release based on the usage of Generative Adversarial Networks (GANs). Our framework uses a part of the privacy budget to train an unsupervised learning model to detect and remove anomalies. We evaluate the performance of the framework using a variety of machine-learning models and metrics. The final results show an improvement of up 13% compared to approaches not using our framework, under the same privacy budget.