<p>The two primarily methodologies discussed in this thesis are the nonparametric entropy-based synthetic timeseries (NEST) and Directed infusion of data (DIOD) algorithms. </p>
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<p>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.</p>
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<p>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). </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22704856 |
| Date | 26 April 2023 |
| Creators | Tyler J Lewis (15361780) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Extending_Synthetic_Data_and_Data_Masking_Procedures_using_Information_Theory/22704856 |
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