From c91a0ff0b62198ba929717f123bf5ec0c1a1cb60 Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Mon, 25 Oct 2021 01:47:21 +0200 Subject: [PATCH] problem: OCD --- text/problem/thething/problem.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text/problem/thething/problem.tex b/text/problem/thething/problem.tex index 90b22a2..db43148 100644 --- a/text/problem/thething/problem.tex +++ b/text/problem/thething/problem.tex @@ -80,7 +80,7 @@ Theorem~\ref{theor:thething-prv} states how to achieve the desired privacy goal \begin{theorem} [{\Thething} privacy] \label{theor:thething-prv} - Let $\mathcal{M}$ be a mechanism with input a time series $S_T$, where $T$ is the set of the involved timestamps, and $L \subseteq T$ be the set of {\thething} timestamps. + Let $\mathcal{M}$ be a mechanism with input a time series $S_T$, where $T$ is the set of the involved timestamps, and $L \subseteq T$ be the set of {\thething} timestamps. $\mathcal{M}$ is decomposed to $\varepsilon$-differential private sub-mechanisms $\mathcal{M}_t$, for every $t \in T$, which apply independent randomness to the event at $t$. Then, given a privacy budget $\varepsilon$, $\mathcal{M}$ satisfies {\thething} privacy if for any $t$ it holds that $$ \sum_{i\in L \cup \{t\}} \varepsilon_i \leq \varepsilon$$