Slides here
Review article for the project : https://arxiv.org/pdf/2104.06523.pdf
References :
- [https://www.sciencedirect.com/science/article/pii/S0925231222004349?casa_token=h33V_myXALoAAAAA:nlO6nHDCZ8mErnJjINUWaJDyKiqW3Ta68q7nLnTe_oJmMCElMMR4g9PJWzCPf5BILuMckd7-LgeD] (https://www.sciencedirect.com/science/article/pii/S0925231222004349?casa_token=h33V_myXALoAAAAA:nlO6nHDCZ8mErnJjINUWaJDyKiqW3Ta68q7nLnTe_oJmMCElMMR4g9PJWzCPf5BILuMckd7-LgeD)
- [Synthetic Data – Anonymisation Groundhog Day] (https://arxiv.org/pdf/2011.07018.pdf)
- [DataSynthesizer: Privacy-Preserving Synthetic Datasets] (https://dl.acm.org/doi/pdf/10.1145/3085504.3091117)
- [PrivBayes: Private Data Release via Bayesian Networks] (https://dl.acm.org/doi/pdf/10.1145/3134428)
- [synthpop: Bespoke Creation of Synthetic Data in R] (https://www.jstatsoft.org/article/view/v074i11)
- [Modeling Tabular data using Conditional GAN] (https://papers.nips.cc/paper/2019/hash/254ed7d2de3b23ab10936522dd547b78-Abstract.html)