meanDP {DPpack} R Documentation

## 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,
)


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

### Value

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

### References

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, https://doi.org/10.1007/11681878_14.

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.

### 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)



[Package DPpack version 0.0.11 Index]