Algorithm 2: Gaussian Noise Algorithm

Input: Dataset D with N samples and F features

Output: Noisy dataset D’ with N samples and F features

1: Define noise term s, which scales the standard deviation of the noise distribution $x_f$

2: For each $f \in F:$

3:   $d_f=column f of Dataset D$

4:   ${\mu }_f=\frac{sum\left(d_f\right)}{N}$

5:   ${\sigma }_f=\sqrt{\frac{\{{\sum }_{n=0}^N{d}_{n,f}-{\mu }_f) }{N-1}}$

6:   $q=s\ast {\sigma }_f$

7:   $x_f~N\left(0,q^2\right)$

8:   $d_f^{\text{'}}=d_f+x_f$

9: End for loop