Function opendp::measurements::then_base_laplace

source ·

pub fn then_base_laplace<D>(
    scale: D::Atom,
    k: Option<i32>
) -> PartialMeasurement<D, D::Carrier, D::InputMetric, MaxDivergence<D::Atom>>where
    D: BaseLaplaceDomain,
    (D, D::InputMetric): MetricSpace,
    D::Atom: Float + SampleDiscreteLaplaceZ2k,
    i32: ExactIntCast<<D::Atom as FloatBits>::Bits>,

Expand description

Make a Measurement that adds noise from the Laplace(scale) distribution to a scalar value.

Valid inputs for input_domain and input_metric are:

`input_domain`	input type	`input_metric`
`atom_domain(T)` (default)	`T`	`absolute_distance(T)`
`vector_domain(atom_domain(T))`	`Vec<T>`	`l1_distance(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

input_domain - Domain of the data type to be privatized.
input_metric - Metric of the data type to be privatized.
scale - Noise scale parameter for the laplace distribution. scale == standard_deviation / sqrt(2).
k - The noise granularity in terms of 2^k.