Struct opendp::measures::MaxDivergence

pub struct MaxDivergence<Q>(PhantomData<Q>);

$\epsilon$-pure differential privacy.

The greatest divergence between any randomly selected subset of the support.

Proof Definition

d-closeness

For any two vectors $u, v \in \texttt{D}$ and any $d$ of generic type $\texttt{Q}$, we say that $M(u), M(v)$ are $d$-close under the max divergence measure (abbreviated as $D_{\infty}$) whenever

D_{\infty}(M(u) \| M(v)) = \max_{S \subseteq \textrm{Supp}(Y)} \Big[\ln \dfrac{\Pr[M(u) \in S]}{\Pr[M(v) \in S]} \Big] \leq d.

Tuple Fields

0: PhantomData<Q>

Trait Implementations

impl<Q> AmplifiableMeasure for MaxDivergence<Q>where Q: ExactIntCast<usize> + InfMul + InfExpM1 + InfLn1P + InfDiv + Clone,

fn amplify( &self, epsilon: &Q, population_size: usize, sample_size: usize ) -> Fallible<Q>

impl<Q: InfAdd + Zero + Clone> BasicCompositionMeasure for MaxDivergence<Q>

fn compose(&self, d_i: Vec<Self::Distance>) -> Fallible<Self::Distance>

impl<Q> Clone for MaxDivergence<Q>

fn clone(&self) -> Self

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<Q> Debug for MaxDivergence<Q>

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<Q> Default for MaxDivergence<Q>

fn default() -> Self

Returns the “default value” for a type.

impl<Q> Measure for MaxDivergence<Q>

type Distance = Q

Proof Definition

impl<Q> PartialEq<MaxDivergence<Q>> for MaxDivergence<Q>

fn eq(&self, _other: &Self) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.