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What is Differentially Private Data Synthesis about?

This paper examines differentially private data synthesis (DIPS) methods for releasing synthetic datasets while protecting individual privacy. It compares various DIPS techniques in terms of their statistical utility and inferential properties through simulation studies, highlighting the practical feasibility and potential applications of these methods. The authors also suggest future research directions in the field of differentially private data synthesis.

Author

Chaimae el harkaoui

Language

EN