PrivReg: Private regression with vertically partitioned data
In vankesteren/privreg: Privacy-preserving Regression

PrivReg

R Documentation

Private regression with vertically partitioned data

Description

Perform privacy-preserving regression modeling across different institutions. This class implements regression with gaussian and binomial responses using block coordinate descent.

Value

an R6 object of class PrivReg

Usage

alice <- PrivReg$new(
  formula,
  data,
  family    = "gaussian",
  name      = "alice",
  verbose   = FALSE,
  debug     = FALSE,
  crypt_key = "testkey"
)

alice$listen()
alice$connect(127.0.0.1)
alice$disconnect()

alice$estimate()
alice$calculate_se()

alice$summary()
alice$coef()
alice$converged()
alice$plot_paths()
alice$elapsed()

Arguments

formula model formula for the regression model at this institution
data data frame for the variables in the model formula
family response family as in glm. Currently only gaussian and binomial are supported
intercept whether to include the intercept. Always use this instead of + 0 in the model formula
name name of this institution
verbose whether to print information
debug whether to print debug statements
crypt_key pre-shared key used to encrypt communication

Details

$new() instantiates and returns a new PrivReg object.
$listen() listens for incoming connections from a partner institution
$connect() connects to a listening partner institution
$disconnect() disconnects from the partner institution
$set_control() sets control parameters. See below for more info
$estimate() computes parameter estimates through block coordinate descent
$calculate_se() computes standard errors using projection method
$converged() test whether the algorithm has converged
$summary() displays a summary of the object, invisibly returns the coef matrix
$coef() returns the model coefficients
$plot_paths() plots the paths of the parameters over the estimation iterations
$elapsed() print information about the elapsed time

Control parameters

max_iter maximum number of iterations of the coordinate descent algorithm
tol PrivReg is converged if all beta changes are below tol.
se Whether to compute standard errors

Examples

## Not run: 
# generate some data
set.seed(45)
X <- matrix(rnorm(1000), 100)
b <- runif(10, -1, 1)
y <- X %*% b + rnorm(100, sd = sqrt(b %*% S %*% b))

# split into alice and bob institutions
alice_data <- data.frame(y, X[, 1:5])
bob_data   <- data.frame(y, X[, 6:10])

# create connection
alice$listen()
bob$connect("127.0.0.1") # if alice is on different computer, change ip

# estimate
alice$estimate()

# ...

# compare results to lm()
summary(lm(y ~ X + 0))
alice$summary()
bob$summary()

## End(Not run)

vankesteren/privreg documentation built on March 4, 2024, 10:47 a.m.