From 3b2d6311ff890bda8ae04f648a09c5b8fcb1d6cb Mon Sep 17 00:00:00 2001 From: Yuchen Pei Date: Wed, 13 Mar 2019 21:35:41 +0100 Subject: fix --- posts/2019-03-13-a-tail-of-two-densities.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) (limited to 'posts') diff --git a/posts/2019-03-13-a-tail-of-two-densities.md b/posts/2019-03-13-a-tail-of-two-densities.md index 0ce47dd..a695af3 100644 --- a/posts/2019-03-13-a-tail-of-two-densities.md +++ b/posts/2019-03-13-a-tail-of-two-densities.md @@ -234,7 +234,7 @@ $$\begin{aligned} \mathbb P(L(q || p) \le \epsilon) &< 1 - \delta \end{aligned}$$ -$, and +**Proof**. Here\'s a example. Let $Y = \{0, 1\}$, and $p(0) = q(1) = 2 / 5$ and $p(1) = q(0) = 3 / 5$. Then it is not hard to verify that $p$ and $q$ are $(\log {4 \over 3}, {1 \over 3})$-ind: just check (3) for all four possible $S \subset Y$ and (4) holds by symmetry. @@ -302,16 +302,16 @@ $|\log p_{|E}(y) - \log q_{|F}(y)| \le \epsilon$ for all $y \in Y$. Here $p_{|E}$ (resp. $q_{|F}$) is $p$ (resp. $q$) conditioned on event $E$ (resp. $F$). -**C2** and +**Remark**. Note that the events in **C2** and **C3** are in different spaces, and therefore we can not write $p_{|E}(S)$ as $p(S | E)$ or $q_{|F}(S)$ as $q(S | F)$. In fact, if we let $E$ and $F$ in **C3** be subsets of $Y$ with $p(E), q(F) \ge 1 - \delta$ and assume $p$ and $q$ have the same supports, then **C3** degenerates to a stronger condition than -$ and +**C2**. Indeed, in this case $p_E(y) = p(y) 1_{y \in E}$ and $q_F(y) = q(y) 1_{y \in F}$, and so $p_E(y) \le e^\epsilon q_F(y)$ forces $E \subset F$. We also obtain $F \subset E$ in the same way. This -gives us $E = F$, and **C2** with +gives us $E = F$, and **C3** becomes **C2** with $A = B = E = F$. As it turns out, **C3** is the condition we need. @@ -422,7 +422,7 @@ To prove the adaptive composition theorem for approximate differential privacy, we need a similar claim (We use index shorthand $\xi_{< i} = \xi_{1 : i - 1}$ and similarly for other notations): -$ be random +**Claim 5**. Let $\xi_{1 : i}$ and $\eta_{1 : i}$ be random variables. Let $$\begin{aligned} @@ -956,7 +956,7 @@ such that for each $i$ and $y_{1 : i}$, $M_i(y_{1 : i})$ is $(\epsilon, 0)$-dp. Then the adpative composition of $M_{1 : k}$ is $(k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon)), \beta)$-dp. - M(x)$ +**Proof**. As before, let $\xi_{1 : k} \overset{d}{=} M(x)$ and $\eta_{1 : k} \overset{d}{=} M(x')$, where $M$ is the adaptive composition of $M_{1 : k}$. Let $p_i$ (resp. $q_i$) be the law of $\xi_i | \xi_{< i}$ (resp. $\eta_i | \eta_{< i}$). Let $p^i$ (resp. @@ -995,7 +995,7 @@ $M_1, ..., M_k$ be $(\epsilon, \delta)$-dp, then the independent composition $M$ of $M_{1 : k}$ is $(k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} \epsilon, k \delta + \beta)$-dp. -$ and +**Proof**. By Claim 4, there exist events $E_{1 : k}$ and $F_{1 : k}$ such that 1. The laws $p_{i | E_i}$ and $q_{i | F_i}$ are $\epsilon$-ind. @@ -1025,7 +1025,7 @@ $i$ and $y_{1 : i}$, $M_i(y_{1 : i})$ is $(\epsilon, \delta)$-dp. Then the adpative composition of $M_{1 : k}$ is $(k a(\epsilon) + \sqrt{2 k \log \beta^{-1}} (\epsilon + a(\epsilon)), \beta + k \delta)$-dp. -$ and +**Proof**. By Claim 5, there exist events $E_{1 : k}$ and $F_{1 : k}$ such that 1. The laws $p_{i | E_i}(\cdot | y_{< i})$ and -- cgit v1.2.3