X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rairo15.git/blobdiff_plain/49ca2ad778013ab14ba1e6f9ab70697a97557e26..17bd6f6df9a94a46128919486c226ae8b1124740:/preliminaries.tex?ds=inline diff --git a/preliminaries.tex b/preliminaries.tex index 3b559b1..628cd44 100644 --- a/preliminaries.tex +++ b/preliminaries.tex @@ -51,30 +51,6 @@ Figure~\ref{fig:iteration:f*}. \end{figure} \end{xpl} -% \vspace{-0.5em} -% It is easy to associate a Markov Matrix $M$ to such a graph $G(f)$ -% as follows: - -% $M_{ij} = \frac{1}{n}$ if there is an edge from $i$ to $j$ in $\Gamma(f)$ and $i \neq j$; $M_{ii} = 1 - \sum\limits_{j=1, j\neq i}^n M_{ij}$; and $M_{ij} = 0$ otherwise. - -% \begin{xpl} -% The Markov matrix associated to the function $f^*$ is - -% \[ -% M=\dfrac{1}{3} \left( -% \begin{array}{llllllll} -% 1&1&1&0&0&0&0&0 \\ -% 1&1&0&0&0&1&0&0 \\ -% 0&0&1&1&0&0&1&0 \\ -% 0&1&1&1&0&0&0&0 \\ -% 1&0&0&0&1&0&1&0 \\ -% 0&0&0&0&1&1&0&1 \\ -% 0&0&0&0&1&0&1&1 \\ -% 0&0&0&1&0&1&0&1 -% \end{array} -% \right) -% \] -%\end{xpl} Let thus be given such kind of map. This article focusses on studying its iterations according to @@ -87,42 +63,77 @@ edges $(v,v)$ for any $v \in \Bool^n$. Next, if we add probabilities on the transition graph, iterations can be interpreted as Markov chains. +\begin{xpl} +Let us consider for instance +the graph $\Gamma(f)$ defined +in \textsc{Figure~\ref{fig:iteration:f*}.} and +the probability function $p$ defined on the set of edges as follows: +$$ +p(e) \left\{ +\begin{array}{ll} += \frac{2}{3} \textrm{ if $e=(v,v)$ with $v \in \Bool^3$,}\\ += \frac{1}{6} \textrm{ otherwise.} +\end{array} +\right. +$$ +The matrix $P$ of the Markov chain associated to the function $f^*$ and to its probability function $p$ is +\[ +P=\dfrac{1}{6} \left( +\begin{array}{llllllll} +4&1&1&0&0&0&0&0 \\ +1&4&0&0&0&1&0&0 \\ +0&0&4&1&0&0&1&0 \\ +0&1&1&4&0&0&0&0 \\ +1&0&0&0&4&0&1&0 \\ +0&0&0&0&1&4&0&1 \\ +0&0&0&0&1&0&4&1 \\ +0&0&0&1&0&1&0&4 +\end{array} +\right) +\] +\end{xpl} -Let $\pi$, $\mu$ be two distribution on a same set $\Omega$. The total +More generally, let $\pi$, $\mu$ be two distribution on $\Bool^n$. The total variation distance between $\pi$ and $\mu$ is denoted $\tv{\pi-\mu}$ and is defined by -$$\tv{\pi-\mu}=\max_{A\subset \Omega} |\pi(A)-\mu(A)|.$$ It is known that -$$\tv{\pi-\mu}=\frac{1}{2}\sum_{x\in\Omega}|\pi(x)-\mu(x)|.$$ Moreover, if -$\nu$ is a distribution on $\Omega$, one has +$$\tv{\pi-\mu}=\max_{A\subset \Bool^n} |\pi(A)-\mu(A)|.$$ It is known that +$$\tv{\pi-\mu}=\frac{1}{2}\sum_{x\in\Bool^n}|\pi(x)-\mu(x)|.$$ Moreover, if +$\nu$ is a distribution on $\Bool^n$, one has $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$ -Let $P$ be the matrix of a markov chain on $\Omega$. $P(x,\cdot)$ is the +Let $P$ be the matrix of a markov chain on $\Bool^n$. $P(x,\cdot)$ is the distribution induced by the $x$-th row of $P$. If the markov chain induced by $P$ has a stationary distribution $\pi$, then we define -$$d(t)=\max_{x\in\Omega}\tv{P^t(x,\cdot)-\pi},$$ -and +$$d(t)=\max_{x\in\Bool^n}\tv{P^t(x,\cdot)-\pi}.$$ +It is known that $d(t+1)\leq d(t)$. \JFC{référence ? Cela a-t-il +un intérêt dans la preuve ensuite.} -$$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ -One can prove that -$$t_{\rm mix}(\varepsilon)\leq \lceil\log_2(\varepsilon^{-1})\rceil t_{\rm mix}(\frac{1}{4})$$ -It is known that $d(t+1)\leq d(t)$. +%and +% $$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ +% One can prove that \JFc{Ou cela a-t-il été fait?} +% $$t_{\rm mix}(\varepsilon)\leq \lceil\log_2(\varepsilon^{-1})\rceil t_{\rm mix}(\frac{1}{4})$$ -Let $(X_t)_{t\in \mathbb{N}}$ be a sequence of $\Omega$ valued random +Let $(X_t)_{t\in \mathbb{N}}$ be a sequence of $\Bool^n$ valued random variables. A $\mathbb{N}$-valued random variable $\tau$ is a {\it stopping time} for the sequence $(X_i)$ if for each $t$ there exists $B_t\subseteq -\omega^{t+1}$ such that $\{tau=t\}=\{(X_0,X_1,\ldots,X_t)\in B_t\}$. +\Omega^{t+1}$ such that $\{\tau=t\}=\{(X_0,X_1,\ldots,X_t)\in B_t\}$. +In other words, the event $\{\tau = t \}$ only depends on the values of +$(X_0,X_1,\ldots,X_t)$, not on $X_k$ with $k > t$. + + +\JFC{Je ne comprends pas la definition de randomized stopping time, Peut-on enrichir ?} Let $(X_t)_{t\in \mathbb{N}}$ be a markov chain and $f(X_{t-1},Z_t)$ a random mapping representation of the markov chain. A {\it randomized stopping time} for the markov chain is a stopping time for -$(Z_t)_{t\in\mathbb{N}}$. It he markov chain is irreductible and has $\pi$ -as stationary distribution, then a {\it stationay time} $\tau$ is a +$(Z_t)_{t\in\mathbb{N}}$. If the markov chain is irreductible and has $\pi$ +as stationary distribution, then a {\it stationary time} $\tau$ is a randomized stopping time (possibily depending on the starting position $x$), such that the distribution of $X_\tau$ is $\pi$: $$\P_x(X_\tau=y)=\pi(y).$$ @@ -130,30 +141,30 @@ $$\P_x(X_\tau=y)=\pi(y).$$ \JFC{Ou ceci a-t-il ete prouvé} \begin{Theo} -If $\tau$ is a strong stationary time, then $d(t)\leq \max_{x\in\Omega} +If $\tau$ is a strong stationary time, then $d(t)\leq \max_{x\in\Bool^n} \P_x(\tau > t)$. \end{Theo} -% Let us first recall the \emph{Total Variation} distance $\tv{\pi-\mu}$, -% which is defined for two distributions $\pi$ and $\mu$ on the same set -% $\Omega$ by: -% $$\tv{\pi-\mu}=\max_{A\subset \Omega} |\pi(A)-\mu(A)|.$$ -% It is known that -% $$\tv{\pi-\mu}=\frac{1}{2}\sum_{x\in\Omega}|\pi(x)-\mu(x)|.$$ - -% Let then $M(x,\cdot)$ be the -% distribution induced by the $x$-th row of $M$. If the Markov chain -% induced by -% $M$ has a stationary distribution $\pi$, then we define -% $$d(t)=\max_{x\in\Omega}\tv{M^t(x,\cdot)-\pi}.$$ -Intuitively $d(t)$ is the largest deviation between -the distribution $\pi$ and $M^t(x,\cdot)$, which -is the result of iterating $t$ times the function. -Finally, let $\varepsilon$ be a positive number, the \emph{mixing time} -with respect to $\varepsilon$ is given by -$$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ -It defines the smallest iteration number -that is sufficient to obtain a deviation lesser than $\varepsilon$. +% % Let us first recall the \emph{Total Variation} distance $\tv{\pi-\mu}$, +% % which is defined for two distributions $\pi$ and $\mu$ on the same set +% % $\Bool^n$ by: +% % $$\tv{\pi-\mu}=\max_{A\subset \Bool^n} |\pi(A)-\mu(A)|.$$ +% % It is known that +% % $$\tv{\pi-\mu}=\frac{1}{2}\sum_{x\in\Bool^n}|\pi(x)-\mu(x)|.$$ + +% % Let then $M(x,\cdot)$ be the +% % distribution induced by the $x$-th row of $M$. If the Markov chain +% % induced by +% % $M$ has a stationary distribution $\pi$, then we define +% % $$d(t)=\max_{x\in\Bool^n}\tv{M^t(x,\cdot)-\pi}.$$ +% Intuitively $d(t)$ is the largest deviation between +% the distribution $\pi$ and $M^t(x,\cdot)$, which +% is the result of iterating $t$ times the function. +% Finally, let $\varepsilon$ be a positive number, the \emph{mixing time} +% with respect to $\varepsilon$ is given by +% $$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ +% It defines the smallest iteration number +% that is sufficient to obtain a deviation lesser than $\varepsilon$. % Notice that the upper and lower bounds of mixing times cannot % directly be computed with eigenvalues formulae as expressed % in~\cite[Chap. 12]{LevinPeresWilmer2006}. The authors of this latter work @@ -162,11 +173,11 @@ that is sufficient to obtain a deviation lesser than $\varepsilon$. -Let us finally present the pseudorandom number generator $\chi_{\textit{14Secrypt}}$ +Let us finally present the pseudorandom number generator $\chi_{\textit{15Rairo}}$ which is based on random walks in $\Gamma(f)$. More precisely, let be given a Boolean map $f:\Bool^n \rightarrow \Bool^n$, a PRNG \textit{Random}, -an integer $b$ that corresponds to an awaited mixing time, and +an integer $b$ that corresponds an iteration number (\textit{i.e.}, the lenght of the walk), and an initial configuration $x^0$. Starting from $x^0$, the algorithm repeats $b$ times a random choice of which edge to follow and traverses this edge. @@ -175,7 +186,6 @@ This PRNG is formalized in Algorithm~\ref{CI Algorithm}. -\vspace{-1em} \begin{algorithm}[ht] %\begin{scriptsize} \KwIn{a function $f$, an iteration number $b$, an initial configuration $x^0$ ($n$ bits)} @@ -183,21 +193,23 @@ This PRNG is formalized in Algorithm~\ref{CI Algorithm}. $x\leftarrow x^0$\; \For{$i=0,\dots,b-1$} { +\If{$\textit{Random}(1) \neq 0$}{ $s\leftarrow{\textit{Random}(n)}$\; $x\leftarrow{F_f(s,x)}$\; } +} return $x$\; %\end{scriptsize} -\caption{Pseudo Code of the $\chi_{\textit{14Secrypt}}$ PRNG} +\caption{Pseudo Code of the $\chi_{\textit{15Rairo}}$ PRNG} \label{CI Algorithm} \end{algorithm} -\vspace{-0.5em} -This PRNG is a particularized version of Algorithm given in~\cite{BCGR11}. -Compared to this latter, the length of the random -walk of our algorithm is always constant (and is equal to $b$) whereas it -was given by a second PRNG in this latter. -However, all the theoretical results that are given in~\cite{BCGR11} remain -true since the proofs do not rely on this fact. + + +This PRNG is a particularized version of Algorithm given in~\cite{DBLP:conf/secrypt/CouchotHGWB14}. +As this latter, the length of the random +walk of our algorithm is always constant (and is equal to $b$). +However, in the current version, we add the constraint that + Let $f: \Bool^{n} \rightarrow \Bool^{n}$. It has been shown~\cite[Th. 4, p. 135]{BCGR11}} that @@ -209,5 +221,5 @@ if and only if its Markov matrix is a doubly stochastic matrix. Let us now present a method to generate functions with Doubly Stochastic matrix and Strongly Connected iteration graph, - denoted as DSSC matrix. +denoted as DSSC matrix.