preliminaries maj

diff --git a/biblio.bib b/biblio.bib
index 7a6d51f93efe37de7201b0fa7a43910a11329cff..d8269e8e8d1cfe71a784c0a0862f75e53ebd5442 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -939,3 +939,34 @@ Pierre-Cyrille, and Wang, Qianxue and Bahi, Jacques},
   pages =      "267--272",
   URL =        "http://dx.doi.org/10.1016/j.ipl.2008.10.015",
 }
   pages =      "267--272",
   URL =        "http://dx.doi.org/10.1016/j.ipl.2008.10.015",
 }

+@inproceedings{DBLP:conf/secrypt/CouchotHGWB14,
+  author    = {Jean{-}Fran{\c{c}}ois Couchot and
+               Pierre{-}Cyrille H{\'{e}}am and
+               Christophe Guyeux and
+               Qianxue Wang and
+               Jacques M. Bahi},
+  title     = {Pseudorandom Number Generators with Balanced Gray Codes},
+  booktitle = {{SECRYPT} 2014 - Proceedings of the 11th International Conference
+               on Security and Cryptography, Vienna, Austria, 28-30 August, 2014},
+  pages     = {469--475},
+  year      = {2014},
+  crossref  = {DBLP:conf/secrypt/2014},
+  timestamp = {Thu, 02 Oct 2014 14:26:58 +0200},
+  biburl    = {http://dblp1.uni-trier.de/rec/bib/conf/secrypt/CouchotHGWB14},
+  bibsource = {dblp computer science bibliography, http://dblp.org}
+}
+
+@proceedings{DBLP:conf/secrypt/2014,
+  editor    = {Mohammad S. Obaidat and
+               Andreas Holzinger and
+               Pierangela Samarati},
+  title     = {{SECRYPT} 2014 - Proceedings of the 11th International Conference
+               on Security and Cryptography, Vienna, Austria, 28-30 August, 2014},
+  publisher = {SciTePress},
+  year      = {2014},
+  isbn      = {978-989-758-045-1},
+  timestamp = {Sat, 05 Dec 4456268 23:53:52 +},
+  biburl    = {http://dblp1.uni-trier.de/rec/bib/conf/secrypt/2014},
+  bibsource = {dblp computer science bibliography, http://dblp.org}
+}
+

diff --git a/main.tex b/main.tex
index 4bbcc0e0396483d8cd6e7a1592cf12d3cd8a02e0..efbc870021ddc089d08b8ccc98249b1e2a51c8c7 100644 (file)
--- a/main.tex
+++ b/main.tex
@@ -82,7 +82,7 @@
 \newcommand{\Gall}[0]{\ensuremath{\mathcal{G}}}
 
 
 \newcommand{\Gall}[0]{\ensuremath{\mathcal{G}}}
 
 

-\newcommand{\JFC}[1]{\begin{color}{green}\textit{}\end{color}}
+\newcommand{\JFC}[1]{\begin{color}{green}\textit{#1}\end{color}}

 \newcommand{\CG}[1]{\begin{color}{blue}\textit{}\end{color}}
 \newcommand{\og}[0]{``}
 \newcommand{\fg}[1]{''}
 \newcommand{\CG}[1]{\begin{color}{blue}\textit{}\end{color}}
 \newcommand{\og}[0]{``}
 \newcommand{\fg}[1]{''}

diff --git a/preliminaries.tex b/preliminaries.tex
index 3b559b11ba0f7867ff7876da30b226e7f1d8e458..628cd446996d1436e2bfb6923e5fc3f3c9ffe6b6 100644 (file)
--- a/preliminaries.tex
+++ b/preliminaries.tex
@@ -51,30 +51,6 @@ Figure~\ref{fig:iteration:f*}.
 \end{figure}
 \end{xpl}
 
 \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
 
 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.
 
 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
 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

 $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$
 
 $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$
 

-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
 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
 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
 
 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 $\pi$:
 $$\P_x(X_\tau=y)=\pi(y).$$
 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}
 
 \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}
 
 \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  
 % 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},
 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.
 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)}
 \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{$i=0,\dots,b-1$}
 {
 $x\leftarrow x^0$\;
 \For{$i=0,\dots,b-1$}
 {

+\If{$\textit{Random}(1) \neq 0$}{

 $s\leftarrow{\textit{Random}(n)}$\;
 $x\leftarrow{F_f(s,x)}$\;
 }
 $s\leftarrow{\textit{Random}(n)}$\;
 $x\leftarrow{F_f(s,x)}$\;
 }

+}

 return $x$\;
 %\end{scriptsize}
 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}
 \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 
 
 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,
 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.