Function opendp::measurements::make_base_gaussian

pub fn make_base_gaussian<D, MO>(
    scale: D::Atom,
    k: Option<i32>
) -> Fallible<Measurement<D, D::Carrier, D::InputMetric, MO>>where
    D: GaussianDomain,
    D::Atom: Float + SampleDiscreteGaussianZ2k,
    (D, D::InputMetric): MetricSpace,
    MO: GaussianMeasure<D>,
    i32: ExactIntCast<<D::Atom as FloatBits>::Bits>,

Make a Measurement that adds noise from the gaussian(scale) distribution to the input.

Set D to change the input data type and input metric:

D	input type	D::InputMetric
AtomDomain<T> (default)	T	AbsoluteDistance<T>
VectorDomain<AtomDomain<T>>	Vec<T>	L2Distance<T>

This function takes a noise granularity in terms of 2^k. Larger granularities are more computationally efficient, but have a looser privacy map. If k is not set, k defaults to the smallest granularity.

Arguments

• scale - Noise scale parameter for the gaussian distribution. scale == standard_deviation.
• k - The noise granularity in terms of 2^k.

Generics

• D - Domain of the data type to be privatized. Valid values are VectorDomain<AtomDomain<T>> or AtomDomain<T>.
• MO - Output Measure. The only valid measure is ZeroConcentratedDivergence<T>.