phi.gaussian {DPpack} | R Documentation |
This function maps an input data row x with a given prefilter to an output value in such a way as to approximate the Gaussian kernel (Chaudhuri et al. 2011).
phi.gaussian(x, theta)
x |
Vector or matrix corresponding to one row of the dataset X. |
theta |
Randomly sampled prefilter vector of length n+1, where n is the length of x. |
Mapped value corresponding to one element of the transformed space.
Chaudhuri K, Monteleoni C, Sarwate AD (2011). “Differentially Private Empirical Risk Minimization.” Journal of Machine Learning Research, 12(29), 1069-1109. https://jmlr.org/papers/v12/chaudhuri11a.html.
x <- c(1,2,3)
theta <- c(0.1, 1.1, -0.8, 3)
phi.gaussian(x, theta)