In the context of differential privacy mechanisms, the choice between using Gaussian noise and Laplace noise can have significant implications for accuracy. When it comes to selection mechanisms like Report Noisy Max and Above Threshold, Gaussian noise tends to provide better accuracy compared to Laplace noise. This is because Gaussian noise has a higher concentration around the mean and thinner tails, allowing for more precise adjustments while still maintaining privacy guarantees.
Gaussian noise is advantageous in many differential privacy mechanisms as it offers a good balance between accuracy and privacy. The mathematical analysis of privacy guarantees with Gaussian noise can be simpler than with Laplace noise due to its properties. However, it's essential to note that while Gaussian noise may improve accuracy, it also requires careful calibration to ensure that the desired level of privacy is maintained.