Struct opendp::measures::SmoothedMaxDivergence

source · [−]
pub struct SmoothedMaxDivergence<Q>(PhantomData<Q>);
Expand description

$\epsilon(\delta)$-approximate differential privacy.

The greatest divergence between any randomly selected subset of the support, with an additive tolerance for error.

The distance $d$ is of type SMDCurve, so it can be invoked with a $\delta$ to retrieve the tightest corresponding $\epsilon$.

Proof Definition

d-closeness

For any two vectors $u, v \in \texttt{D}$ and any choice of $\epsilon, \delta$ such that $\epsilon \ge d(\delta)$, we say that $M(u), M(v)$ are $d$-close under the smoothed max divergence measure (abbreviated as $D_{S\infty}$) whenever

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

Tuple Fields

0: PhantomData<Q>

Trait Implementations

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