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Wasserstein Generative Adversarial Privacy Networks

Mulder, K.E. (2019) Wasserstein Generative Adversarial Privacy Networks.

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Abstract:A method to filter private data from public data using generative adversarial networks has been introduced in an article "Generative Adversarial Privacy" by Chong Huang et al. in 2018. We attempt to reproduce their results, and build further upon their work by introducing a new variant based on Wasserstein generative adversarial networks. For certain classes of probability distributions, we prove theorems relating the 1-Wasserstein distance to the amount of private data leaked, and provide counterexamples showing that this relation is not trivial.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:http://purl.utwente.nl/essays/79054
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