meanDataAccess: Differentially Private Mean Data Access Function

In DPpack: Differentially Private Statistical Analysis and Machine Learning

Differentially Private Mean Data Access Function

Description

This function performs the data access step in the computation of a differentially private mean. The true values are computed using mean, while the sensitivities are calculated based on bounded and unbounded differential privacy \insertCiteKifer2011DPpack according to the theoretical values \insertCiteLiu2019bDPpack. For the mean, the sensitivities based on bounded and unbounded differential privacy are identical, so only one value is returned.

Usage

meanDataAccess(x, lower.bound, upper.bound)

Arguments

x	Dataset whose mean is desired.
lower.bound	Scalar representing the global or public lower bound on values of x.
upper.bound	Scalar representing the global or public upper bound on values of x.

Value

List of the true mean and the sensitivity calculated based on theoretical values.

References

\insertRef

Liu2019bDPpack

\insertRef

Kifer2011DPpack

Examples

meanDataAccess(c(1,4,3,2), 0, 5)

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