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Large Differentially Private Data Synthesis

Zacharioudakis Christos

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Year 2020
Type of Item Diploma Work
Bibliographic Citation Zacharioudakis Christos, "Large Differentially Private Data Synthesis", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2020
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In our days, data exists in abundance, it is ever increasing and it finds numerous uses. A most recent use is the training of Machine Learning models, software capable of making their own decisions. However, using data to train said models raises significant privacy concerns, especially when it comes to highly sensitive data such as medical records. A solution to this predicament is the synthetic data generation, the production of “fake” data that resembles the real one. However, synthetic data generation does not provide any privacy guarantees on its own. The need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. One such definition is Differential Privacy. This thesis attempts to combine the concept of Differential Privacy with various Machine Learning techniques to generate truly private data that can be utilized in place of the real one effectively. The Machine Learning models that will concern us are the Bayesian Networks and the Generative Adversarial Networks.

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