R/LS_simulateDifferencesDS.R

In transbioZI/dsMTLBase: dsMTL server side base functions

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#
# Package Name: dsMTLBase
# Description: The server-side functions of dsMTL
#
# dsMTL - a computational framework for privacy-preserving, distributed 
#   multi-task machine learning
# Copyright (C) 2021  Han Cao (han.cao@zi-mannheim.de)
# All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# 
################################################################################




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#' @title Computes the gradient and function value of least-squares loss based on the removal of one data point from the original data. Used for sensitivity analyses required for the implementation of differential privacy.
#' @description Calculate the gradient and function value of least-squares loss of a given point based on the removal of one data point from the original data. Used for sensitivity analyses required for the implementation of differential privacy.
#' @param w  The current estimate of coefficient vector
#' @param x  The design matrix    
#' @param y  The outcome vector 

#' @return The gradient and function value of least-squares loss based on the removal of one data point from the original data
#' @details The current estimate of w was sent to target server and applied on {x,y}.

#' @export  
#' @author Roman Schefzik
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LS_simulateDifferencesDS <- function(w, x, y) {
  
  ###calculate values with one row removed
  
  w <- as.numeric(unlist(strsplit(w, split=",")))
  x <- eval(parse(text=x), envir = parent.frame())
  y <- eval(parse(text=y), envir = parent.frame())
  
  nRemove <- 1
  rowsToRemove <- sample(1:nrow(x), nRemove)
  xModified <- x[-rowsToRemove, ]
  yModified <- y[-rowsToRemove]
  
  # Recalculate function value and gradient without the removed row
  y_res = xModified %*% w - yModified
  grad_w <-  base::t(xModified) %*% y_res/nrow(xModified) 
  funcVal <- 0.5 * mean((y_res)^2)
  
  gradWsRemoved<-list(grad_w = grad_w, funcVal = funcVal)
  
  return(gradWsRemoved)
}