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dc.contributor.authorEbrahimianghazani, Faezeh 18:27:32 (GMT) 18:27:32 (GMT)
dc.description.abstractThis thesis proposes DP-Select, a novel approach to tabular data synthesis that combines DP-GAN and differentially private selection. We develop a mutual information-based selection method that is flexible and scalable for high-dimensional data and large numbers of marginals while being differentially private. We evaluate DP-Select on various datasets and demonstrate its effectiveness and utility compared to existing DP-GAN methods. Our results indicate that DP-Select significantly enhances the utility of synthesized data while maintaining strong privacy guarantees, making it a promising extension of DP-GANs for privacy-preserving data synthesis in terms of differential privacy. We also show that DP-Select performs better for smaller privacy budgets, making it an attractive option for scenarios with limited privacy budgets.en
dc.publisherUniversity of Waterlooen
dc.subjectdata synthesisen
dc.subjectdifferential privacyen
dc.subjectgenerative adversarial networken
dc.subjectdifferentially private selectionen
dc.subjectexponential mechanismen
dc.subjectdata privacyen
dc.subjecttabular dataen
dc.titleDP-Select: Improving Utility and Privacy in Tabular Data Synthesis with Differentially Private Generative Adversarial Networks and Differentially Private Selectionen
dc.typeMaster Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorKerschbaum, Florian
uws.contributor.affiliation1Faculty of Mathematicsen

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