Privacy Preserving Synthetic Data Release Using Deep Learning

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How researchers see this paper

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Advances and Open Problems in Federated Learning (2019, background): “• One way around the issue of privacy composition is by producing synthetic data [145, 9], which can then be used indefinitely without incurring additional privacy loss.”
Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, background): “[41] and [42] extend differentially private synthetic data generation to the domain of unstructured data such as images and audio.”
Synthetic data generation for tabular health records: A systematic review (2022, methodology): “Abay 2019 [48] Preserve privacy Numerical Categorical (a)CMC (b)Mamographic Mass (c)Diabetes (d)Breast Cancer PrivateSVM PrivBayes DP-EM DP-VAE DP-SYN (R)Compare probability distributions (U)ML models: TSTR”
Machine Learning for Synthetic Data Generation: a Review (2023, methodology): “Instead of using graphical models as the summarization/representation of a dataset [14], [165], [167], [187], [188], [178] proposes to use a set of large number of low-degree marginals to represent a dataset.”