MDP.Sim: Releasing 4 Differential Private RKHS smoothing means of a...

In sxz155/PFDA:

Description

This function create 4 DP RKHS smoothing means from an existing data set with known eigenvalues and eigenvectors

Usage

MDP.Sim(alpha = rep(1, 1), beta = rep(0.1, 1), kernel, phi, ro, n, N, tau,
  case, pow, mu)

Arguments

alpha, beta	Privacy parameters, real numbers
phi	real number, penalty parameter
ro	range parameter in kernel, real number
n	real vector 4*1, number of grid points
N	real vector 4*1, number of observations
tau	range of the uniform distribution in KL expansion
pow	smoothing parameter, e.val.x_i=i^-pow
mu	real vector n*1, initial mean vector
e.val.x	real vector n*1, eigenvalues
e.vec.x	real valued matrix n*N, eigenvectors
e.val.z	real valued matrix n*N, eigenvectors of noise

Value

four DP_Sim output each of them includin "f.tilda","delta","f" and "X"

sxz155/PFDA documentation built on May 30, 2019, 10:40 p.m.