Extending Synthetic Data and Data Masking Procedures using Information Theory

The two primarily methodologies discussed in this thesis are the nonparametric entropy-based synthetic timeseries (NEST) and Directed infusion of data (DIOD) algorithms. 

The former presents a novel synthetic data algorithm that is shown to outperform sismilar state-of-the-art, including generative networks, in terms of utility and data consistency. Majority of data used are open-source, and are cited where appropriate.

DIOD presents a novel data masking paradigm that presevres the utility, privacy, and efficiency required by the current industrial paradigm, and presents a cheaper alternative to many state-of-the-art. Data used include simulation data (source code cited), equations-based data, and open-source images (cited as needed).