DPpack (version 0.2.2)

meanDP: Differentially Private Mean

Description

This function computes the differentially private mean of a given dataset at user-specified privacy levels of epsilon and delta.

Usage

meanDP(
  x,
  eps,
  lower.bound,
  upper.bound,
  which.sensitivity = "bounded",
  mechanism = "Laplace",
  delta = 0,
  type.DP = "aDP"
)

Value

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

Arguments

x

Dataset whose mean is desired.

eps

Positive real number defining the epsilon privacy budget.

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.

which.sensitivity

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 Kifer2011DPpack. 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.

mechanism

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

delta

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

type.DP

String indicating the type of differential privacy desired for the Gaussian mechanism (if selected). Can be either 'pDP' for probabilistic DP Machanavajjhala2008DPpack or 'aDP' for approximate DP Dwork2006bDPpack. Note that if 'aDP' is chosen, epsilon must be strictly less than 1.

References

Dwork2006aDPpack

Kifer2011DPpack

Machanavajjhala2008DPpack

Dwork2006bDPpack

Examples

D <- stats::rnorm(500, mean=3, sd=2)
lb <- -3 # 3 std devs below mean
ub <- 9 # 3 std devs above mean
meanDP(D,  1, lb, ub)
meanDP(D, .5, lb, ub, which.sensitivity='unbounded', mechanism='Gaussian',
  delta=0.01)