quantileDataAccess {DPpack} | R Documentation |
Differentially Private Quantile Data Access Function
Description
This function performs the data access step in the computation of a differentially private quantile. The utility vector is computed as in Smith (2011), while the sensitivities are calculated based on bounded and unbounded differential privacy (Kifer and Machanavajjhala 2011) according to the theoretical values (Gillenwater et al. 2021).
Usage
quantileDataAccess(x, quant, lower.bound, upper.bound)
Arguments
x |
Numeric vector. |
quant |
Real number between 0 and 1 indicating which quantile to return. |
lower.bound |
Real number giving the lower bound of the input data. |
upper.bound |
Real number giving the upper bound of the input data. |
Value
List of a vector corresponding to the utility function, the sorted and clipped vector of inputs and the sensitivity calculated based on theoretical values.
References
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.
Smith A (2011). “Privacy-Preserving Statistical Estimation with Optimal Convergence Rates.” In Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing, STOC '11, 813–822. ISBN 9781450306911, doi:10.1145/1993636.1993743.
Gillenwater J, Joseph M, Kulesza A (2021). “Differentially Private Quantiles.” In Meila M, Zhang T (eds.), Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, 3713–3722. http://proceedings.mlr.press/v139/gillenwater21a/gillenwater21a.pdf.
Examples
quantileDataAccess(c(1,1,-2,8,-6),.25,-10,10)