meanDataAccess {DPpack} | R Documentation |
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.
meanDataAccess(x, lower.bound, upper.bound)
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. |
List of the true mean and the sensitivity calculated based on theoretical values.
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.
meanDataAccess(c(1,4,3,2), 0, 5)