meanDataAccess {DPpack}R Documentation

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 (Kifer and Machanavajjhala 2011) according to the theoretical values (Liu 2019). 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

Liu F (2019). “Statistical Properties of Sanitized Results from Differentially Private Laplace Mechanism with Univariate Bounding Constraints.” Transactions on Data Privacy, 12(3), 169-195. http://www.tdp.cat/issues16/tdp.a316a18.pdf.

Kifer D, Machanavajjhala A (2011). “No Free Lunch in Data Privacy.” In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD '11, 193–204. ISBN 9781450306614, doi:10.1145/1989323.1989345.

Examples

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


[Package DPpack version 0.2.2 Index]