# Tag Info

## New answers tagged privacy

1

If you are working with discrete probability distributions, then for any subset $S$ of the range, you have $$\Pr[\mathcal{M}(x)\in S] = \sum_{s\in S} \Pr[\mathcal{M}(x)=s] \leq \sum_{s\in S} e^\varepsilon \Pr[\mathcal{M}(x')=s] = e^\varepsilon \Pr[\mathcal{M}(x')\in S]$$ and so the second definition implies the first (and thus they are equivalent). You ...

Top 50 recent answers are included