Scale Analysis and Shape Selection for the Generalized Gaussian Mechanism under Approximate Differential Privacy

Differential privacy provides a rigorous framework for protecting private information, typically achieved by adding random noise to query results. The generalized Gaussian family is a flexible class of additive noise distributions indexed by the shape parameter $p$ and includes the Laplace and Gauss