pooledCovDataAccess: Differentially Private Pooled Covariance Data Access Function

In DPpack: Differentially Private Statistical Analysis and Machine Learning

Differentially Private Pooled Covariance Data Access Function

Description

This function performs the data access step in the computation of a differentially private pooled covariance. The true values are computed using the theoretical formula and cov, while the sensitivities are calculated based on bounded and unbounded differential privacy \insertCiteKifer2011DPpack according to the theoretical values \insertCiteLiu2019bDPpack.

Usage

pooledCovDataAccess(
  samples,
  lower.bound1,
  upper.bound1,
  lower.bound2,
  upper.bound2,
  approx.n.max
)

Arguments

samples	List of two-column matrices from which to compute the pooled covariance.
lower.bound1, lower.bound2	Real numbers giving the lower bounds of the first and second columns of samples, respectively.
upper.bound1, upper.bound2	Real numbers giving the upper bounds of the first and second columns of samples, respectively.
approx.n.max	Logical indicating whether to approximate n.max, which is defined to be the length of the largest input vector. Approximation is best if n.max is very large.

Value

List of the true pooled covariance and the sensitivities calculated based on bounded and unbounded differential privacy.

References

\insertRef

Liu2019bDPpack

\insertRef

Kifer2011DPpack

Examples

x1 <- matrix(c(1,4,-2,8,-6,-3),ncol=2)
x2 <- matrix(c(1,2,-5,7),ncol=2)
pooledCovDataAccess(list(x1,x2),-10,10,-10,10,FALSE)

DPpack documentation built on April 8, 2023, 9:09 a.m.