covDP {DPpack}R Documentation

Differentially Private Covariance


This function computes the differentially private covariance of a pair of vectors at user-specified privacy levels of epsilon and delta.


  which.sensitivity = "bounded",
  mechanism = "Laplace",
  delta = 0,
  type.DP = "aDP"


x1, x2

Numeric vectors whose covariance is desired.


Positive real number defining the epsilon privacy budget.

lower.bound1, lower.bound2

Real numbers giving the global or public lower bounds of x1 and x2, respectively.

upper.bound1, upper.bound2

Real numbers giving the global or public upper bounds of x1 and x2, respectively.


String indicating which type of sensitivity to use. Can be one of 'bounded', 'unbounded', 'both'. If 'bounded' (default), returns result based on bounded definition for differential privacy. If 'unbounded', returns result based on unbounded definition. If 'both', returns result based on both methods (Kifer and Machanavajjhala 2011). Note that if 'both' is chosen, each result individually satisfies (eps, delta)-differential privacy, but may not do so collectively and in composition. Care must be taken not to violate differential privacy in this case.


String indicating which mechanism to use for differential privacy. Currently the following mechanisms are supported: 'Laplace', 'Gaussian'. Default is Laplace. See LaplaceMechanism and GaussianMechanism for a description of the supported mechanisms.


Nonnegative real number defining the delta privacy parameter. If 0 (default), reduces to eps-DP and the Laplace mechanism is used.


String indicating the type of differential privacy desired for the Gaussian mechanism (if selected). Can be either 'pDP' for probabilistic DP (Machanavajjhala et al. 2008) or 'aDP' for approximate DP (Dwork et al. 2006). Note that if 'aDP' is chosen, epsilon must be strictly less than 1.


Sanitized covariance based on the bounded and/or unbounded definitions of differential privacy.


Dwork C, McSherry F, Nissim K, Smith A (2006). “Calibrating Noise to Sensitivity in Private Data Analysis.” In Halevi S, Rabin T (eds.), Theory of Cryptography, 265–284. ISBN 978-3-540-32732-5,

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.

Machanavajjhala A, Kifer D, Abowd J, Gehrke J, Vilhuber L (2008). “Privacy: Theory meets Practice on the Map.” In 2008 IEEE 24th International Conference on Data Engineering, 277-286. doi:10.1109/ICDE.2008.4497436.

Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006). “Our Data, Ourselves: Privacy Via Distributed Noise Generation.” In Vaudenay S (ed.), Advances in Cryptology - EUROCRYPT 2006, 486–503. ISBN 978-3-540-34547-3, doi:10.1007/11761679_29.

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.


D1 <- sort(stats::rnorm(500, mean=3, sd=2))
D2 <- sort(stats::rnorm(500, mean=-1,sd=0.5))
lb1 <- -3 # 3 std devs below mean
lb2 <- -2.5 # 3 std devs below mean
ub1 <- 9 # 3 std devs above mean
ub2 <- .5 # 3 std devs above mean
covDP(D1, D2, 1, lb1, ub1, lb2, ub2)
covDP(D1, D2, .5, lb1, ub1, lb2, ub2, which.sensitivity='unbounded',
  mechanism='Gaussian', delta=0.01)

[Package DPpack version 0.0.11 Index]