pooledCovDataAccess {DPpack} | R Documentation |
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
(Kifer and Machanavajjhala 2011) according to the theoretical values
(Liu 2019).
pooledCovDataAccess(
samples,
lower.bound1,
upper.bound1,
lower.bound2,
upper.bound2,
approx.n.max
)
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. |
List of the true pooled covariance and the sensitivities calculated based on bounded and unbounded differential privacy.
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.
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)