\usepackage[standard]{ntheorem}
\usepackage{algorithmic}
\usepackage{slashbox}
+\usepackage{ctable}
+\usepackage{tabularx}
+\usepackage{multirow}
% Pour mathds : les ensembles IR, IN, etc.
\usepackage{dsfont}
need is to define \emph{secure} generators able to withstand malicious
attacks. Roughly speaking, an attacker should not be able in practice to make
the distinction between numbers obtained with the secure generator and a true random
-sequence.
+sequence. \begin{color}{red} Or, in an equivalent formulation, he or she should not be
+able (in practice) to predict the next bit of the generator, having the knowledge of all the
+binary digits that have been already released. ``Being able in practice'' refers here
+to the possibility to achieve this attack in polynomial time, and to the exponential growth
+of the difficulty of this challenge when the size of the parameters of the PRNG increases.
+\end{color}
+
Finally, a small part of the community working in this domain focuses on a
third requirement, that is to define chaotic generators.
The main idea is to take benefits from a chaotic dynamical system to obtain a
{\it BigCrush} battery of tests, which is widely considered as the most
stringent statistical evaluation of a sequence claimed as random.
This battery can be found in the well-known TestU01 package~\cite{LEcuyerS07}.
+\begin{color}{red}
+More precisely, each time we performed a test on a PRNG, we ran it
+twice in order to observe if all $p-$values are inside [0.01, 0.99]. In
+fact, we observed that few $p-$values (less than ten) are sometimes
+outside this interval but inside [0.001, 0.999], so that is why a
+second run allows us to confirm that the values outside are not for
+the same test. With this approach all our PRNGs pass the {\it
+ BigCrush} successfully and all $p-$values are at least once inside
+[0.01, 0.99].
+\end{color}
Chaos, for its part, refers to the well-established definition of a
chaotic dynamical system proposed by Devaney~\cite{Devaney}.
-
In a previous work~\cite{bgw09:ip,guyeux10} we have proposed a post-treatment on PRNGs making them behave
as a chaotic dynamical system. Such a post-treatment leads to a new category of
PRNGs. We have shown that proofs of Devaney's chaos can be established for this
and on an iteration process called ``chaotic
iterations'' on which the post-treatment is based.
The proposed PRNG and its proof of chaos are given in Section~\ref{sec:pseudorandom}.
-Section~\ref{sec:efficient PRNG} presents an efficient
-implementation of this chaotic PRNG on a CPU, whereas Section~\ref{sec:efficient PRNG
+\begin{color}{red}
+Section~\ref{The generation of pseudorandom sequence} illustrates the statistical
+improvement related to the chaotic iteration based post-treatment, for
+our previously released PRNGs and a new efficient
+implementation on CPU.
+\end{color}
+ Section~\ref{sec:efficient PRNG
gpu} describes and evaluates theoretically the GPU implementation.
Such generators are experimented in
Section~\ref{sec:experiments}.
generator provided by the post-treatment.
Such a proof leads to the proposition of a cryptographically secure and
chaotic generator on GPU based on the famous Blum Blum Shub
-in Section~\ref{sec:CSGPU}, and to an improvement of the
+in Section~\ref{sec:CSGPU}, \begin{color}{red} to a practical
+security evaluation in Section~\ref{sec:Practicak evaluation}, \end{color} and to an improvement of the
Blum-Goldwasser protocol in Sect.~\ref{Blum-Goldwasser}.
This research work ends by a conclusion section, in which the contribution is
summarized and intended future work is presented.
-\section{Related works on GPU based PRNGs}
+\section{Related work on GPU based PRNGs}
\label{section:related works}
Numerous research works on defining GPU based PRNGs have already been proposed in the
two PRNGs as inputs. These two generators are mixed with chaotic iterations,
leading thus to a new PRNG that
\begin{color}{red}
-should improves the statistical properties of each
+should improve the statistical properties of each
generator taken alone.
Furthermore, the generator obtained by this way possesses various chaos properties that none of the generators used as input
present.
First of all, some chaotic iterations have to be done to generate a sequence
$\left(x^n\right)_{n\in\mathds{N}} \in \left(\mathds{B}^{32}\right)^\mathds{N}$
of Boolean vectors, which are the successive states of the iterated system.
-Some of these vectors will be randomly extracted and our pseudo-random bit
+Some of these vectors will be randomly extracted and our pseudorandom bit
flow will be constituted by their components. Such chaotic iterations are
realized as follows. Initial state $x^0 \in \mathds{B}^{32}$ is a Boolean
vector taken as a seed and chaotic strategy $\left(S^n\right)_{n\in\mathds{N}}\in
Such a procedure is equivalent to achieve chaotic iterations with
the Boolean vectorial negation $f_0$ and some well-chosen strategies.
Finally, some $x^n$ are selected
-by a sequence $m^n$ as the pseudo-random bit sequence of our generator.
+by a sequence $m^n$ as the pseudorandom bit sequence of our generator.
$(m^n)_{n \in \mathds{N}} \in \mathcal{M}^\mathds{N}$ is computed from $PRNG_1$, where $\mathcal{M}\subset \mathds{N}^*$ is a finite nonempty set of integers.
The basic design procedure of the New CI generator is summarized in Algorithm~\ref{Chaotic iteration1}.
The internal state is $x$, the output state is $r$. $a$ and $b$ are those computed by the two input
PRNGs. Lastly, the value $g(a)$ is an integer defined as in Eq.~\ref{Formula}.
-This function is required to make the outputs uniform in $\llbracket 0, 2^\mathsf{N}-1 \rrbracket$
-(the reader is referred to~\cite{bg10:ip} for more information).
+This function must be chosen such that the outputs of the resulted PRNG is uniform in $\llbracket 0, 2^\mathsf{N}-1 \rrbracket$. Function of \eqref{Formula} achieves this
+goal (other candidates and more information can be found in ~\cite{bg10:ip}).
\begin{equation}
\label{Formula}
}
\ENDFOR
\STATE$a\leftarrow{PRNG_1()}$\;
-\STATE$m\leftarrow{g(a)}$\;
-\STATE$k\leftarrow{m}$\;
+\STATE$k\leftarrow{g(a)}$\;
\WHILE{$i=0,\dots,k$}
\STATE$b\leftarrow{PRNG_2()~mod~\mathsf{N}}$\;
\forall n \in \mathds{N}^*, x^n = x^{n-1} \oplus S^n,
\end{array}
\right.
-\label{equation Oplus0}
+\label{equation Oplus}
\end{equation}
where $\oplus$ is for the bitwise exclusive or between two integers.
This rewriting can be understood as follows. The $n-$th term $S^n$ of the
component of this state (a binary digit) changes if and only if the $k-$th
digit in the binary decomposition of $S^n$ is 1.
-The single basic component presented in Eq.~\ref{equation Oplus0} is of
+The single basic component presented in Eq.~\ref{equation Oplus} is of
ordinary use as a good elementary brick in various PRNGs. It corresponds
to the following discrete dynamical system in chaotic iterations:
Obviously, replacing the previous CI PRNG Algorithms by
-Equation~\ref{equation Oplus0}, which is possible when the iteration function is
-the vectorial negation, leads to a speed improvement. However, proofs
+Equation~\ref{equation Oplus}, which is possible when the iteration function is
+the vectorial negation, leads to a speed improvement
+(the resulting generator will be referred as ``Xor CI PRNG''
+in what follows).
+However, proofs
of chaos obtained in~\cite{bg10:ij} have been established
only for chaotic iterations of the form presented in Definition
\ref{Def:chaotic iterations}. The question is now to determine whether the
\begin{color}{red}
\section{Statistical Improvements Using Chaotic Iterations}
-\subsection{About some Well-known PRNGs}
-\label{The generation of pseudo-random sequence}
-
+\label{The generation of pseudorandom sequence}
-
-Let us now give illustration on the fact that chaos appears to improve statistical properties.
+Let us now explain why we are reasonable grounds to believe that chaos
+can improve statistical properties.
+We will show in this section that, when mixing defective PRNGs with
+chaotic iterations, the result presents better statistical properties
+(this section summarizes the work of~\cite{bfg12a:ip}).
\subsection{Details of some Existing Generators}
-Here are the modules of PRNGs we have chosen to experiment.
+The list of defective PRNGs we will use
+as inputs for the statistical tests to come is introduced here.
-\subsubsection{LCG}
-This PRNG implements either the simple or the combined linear congruency generator (LCGs). The simple LCG is defined by the recurrence:
+Firstly, the simple linear congruency generator (LCGs) will be used.
+It is defined by the following recurrence:
\begin{equation}
x^n = (ax^{n-1} + c)~mod~m
\label{LCG}
\end{equation}
-where $a$, $c$, and $x^0$ must be, among other things, non-negative and less than $m$~\cite{testU01}. In what follows, 2LCGs and 3LCGs refer as two (resp. three) combinations of such LCGs.
-For further details, see~\cite{combined_lcg}.
+where $a$, $c$, and $x^0$ must be, among other things, non-negative and less than
+$m$~\cite{LEcuyerS07}. In what follows, 2LCGs and 3LCGs refer as two (resp. three)
+combinations of such LCGs. For further details, see~\cite{bfg12a:ip,combined_lcg}.
-\subsubsection{MRG}
-This module implements multiple recursive generators (MRGs), based on a linear recurrence of order $k$, modulo $m$~\cite{testU01}:
+Secondly, the multiple recursive generators (MRGs) will be used too, which
+are based on a linear recurrence of order
+$k$, modulo $m$~\cite{LEcuyerS07}:
\begin{equation}
x^n = (a^1x^{n-1}+~...~+a^kx^{n-k})~mod~m
\label{MRG}
\end{equation}
-Combination of two MRGs (referred as 2MRGs) is also be used in this paper.
+Combination of two MRGs (referred as 2MRGs) is also used in these experimentations.
-\subsubsection{UCARRY}
-Generators based on linear recurrences with carry are implemented in this module. This includes the add-with-carry (AWC) generator, based on the recurrence:
+Generators based on linear recurrences with carry will be regarded too.
+This family of generators includes the add-with-carry (AWC) generator, based on the recurrence:
\begin{equation}
\label{AWC}
\begin{array}{l}
x^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ mod ~ 2^w, \\
c^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ / ~ 2^w. \end{array}\end{equation}
-\subsubsection{GFSR}
-This module implements the generalized feedback shift register (GFSR) generator, that is:
+Then the generalized feedback shift register (GFSR) generator has been implemented, that is:
\begin{equation}
x^n = x^{n-r} \oplus x^{n-k}
\label{GFSR}
\end{equation}
-\subsubsection{INV}
-Finally, this module implements the nonlinear inversive generator, as defined in~\cite{testU01}, which is:
+Finally, the nonlinear inversive generator~\cite{LEcuyerS07} has been regarded too, which is:
\begin{equation}
\label{INV}
+\begin{table}
+\renewcommand{\arraystretch}{1.3}
+\caption{TestU01 Statistical Test}
+\label{TestU011}
+\centering
+ \begin{tabular}{lccccc}
+ \toprule
+Test name &Tests& Logistic & XORshift & ISAAC\\
+Rabbit & 38 &21 &14 &0 \\
+Alphabit & 17 &16 &9 &0 \\
+Pseudo DieHARD &126 &0 &2 &0 \\
+FIPS\_140\_2 &16 &0 &0 &0 \\
+SmallCrush &15 &4 &5 &0 \\
+Crush &144 &95 &57 &0 \\
+Big Crush &160 &125 &55 &0 \\ \hline
+Failures & &261 &146 &0 \\
+\bottomrule
+ \end{tabular}
+\end{table}
+
+
+
+\begin{table}
+\renewcommand{\arraystretch}{1.3}
+\caption{TestU01 Statistical Test for Old CI algorithms ($\mathsf{N}=4$)}
+\label{TestU01 for Old CI}
+\centering
+ \begin{tabular}{lcccc}
+ \toprule
+\multirow{3}*{Test name} & \multicolumn{4}{c}{Old CI}\\
+&Logistic& XORshift& ISAAC&ISAAC \\
+&+& +& + & + \\
+&Logistic& XORshift& XORshift&ISAAC \\ \cmidrule(r){2-5}
+Rabbit &7 &2 &0 &0 \\
+Alphabit & 3 &0 &0 &0 \\
+DieHARD &0 &0 &0 &0 \\
+FIPS\_140\_2 &0 &0 &0 &0 \\
+SmallCrush &2 &0 &0 &0 \\
+Crush &47 &4 &0 &0 \\
+Big Crush &79 &3 &0 &0 \\ \hline
+Failures &138 &9 &0 &0 \\
+\bottomrule
+ \end{tabular}
+\end{table}
-\subsection{Statistical tests}
-\label{Security analysis}
-
-%A theoretical proof for the randomness of a generator is impossible to give, therefore statistical inference based on observed sample sequences produced by the generator seems to be the best option.
-Considering the properties of binary random sequences, various statistical tests can be designed to evaluate the assertion that the sequence is generated by a perfectly random source. We have performed some statistical tests for the CIPRNGs proposed here. These tests include NIST suite~\cite{ANDREW2008} and DieHARD battery of tests~\cite{DieHARD}. For completeness and for reference, we give in the following subsection a brief description of each of the aforementioned tests.
-
-
-
-\subsubsection{NIST statistical tests suite}
-
-Among the numerous standard tests for pseudo-randomness, a convincing way to show the randomness of the produced sequences is to confront them to the NIST (National Institute of Standards and Technology) statistical tests, being an up-to-date tests suite proposed by the Information Technology Laboratory (ITL). A new version of the Statistical tests suite has been released in August 11, 2010.
-
-The NIST tests suite SP 800-22 is a statistical package consisting of 15 tests. They were developed to test the randomness of binary sequences produced by hardware or software based cryptographic pseudorandom number generators. These tests focus on a variety of different types of non-randomness that could exist in a sequence.
-
-For each statistical test, a set of $P-values$ (corresponding to the set of sequences) is produced.
-The interpretation of empirical results can be conducted in various ways.
-In this paper, the examination of the distribution of P-values to check for uniformity ($ P-value_{T}$) is used.
-The distribution of $P-values$ is examined to ensure uniformity.
-If $P-value_{T} \geqslant 0.0001$, then the sequences can be considered to be uniformly distributed.
-
-In our experiments, 100 sequences (s = 100), each with 1,000,000-bit long, are generated and tested. If the $P-value_{T}$ of any test is smaller than 0.0001, the sequences are considered to be not good enough and the generating algorithm is not suitable for usage.
-
+\subsection{Statistical tests}
+\label{Security analysis}
-\subsubsection{DieHARD battery of tests}
-The DieHARD battery of tests has been the most sophisticated standard for over a decade. Because of the stringent requirements in the DieHARD tests suite, a generator passing this battery of
-tests can be considered good as a rule of thumb.
+Three batteries of tests are reputed and usually used
+to evaluate the statistical properties of newly designed pseudorandom
+number generators. These batteries are named DieHard~\cite{Marsaglia1996},
+the NIST suite~\cite{ANDREW2008}, and the most stringent one called
+TestU01~\cite{LEcuyerS07}, which encompasses the two other batteries.
-The DieHARD battery of tests consists of 18 different independent statistical tests. This collection
- of tests is based on assessing the randomness of bits comprising 32-bit integers obtained from
-a random number generator. Each test requires $2^{23}$ 32-bit integers in order to run the full set
-of tests. Most of the tests in DieHARD return a $P-value$, which should be uniform on $[0,1)$ if the input file
-contains truly independent random bits. These $P-values$ are obtained by
-$P=F(X)$, where $F$ is the assumed distribution of the sample random variable $X$ (often normal).
-But that assumed $F$ is just an asymptotic approximation, for which the fit will be worst
-in the tails. Thus occasional $P-values$ near 0 or 1, such as 0.0012 or 0.9983, can occur.
-An individual test is considered to be failed if the $P-value$ approaches 1 closely, for example $P>0.9999$.
-\subsection{Results and discussion}
\label{Results and discussion}
\begin{table*}
\renewcommand{\arraystretch}{1.3}
\end{tabular}
\end{table*}
-Table~\ref{NIST and DieHARD tests suite passing rate the for PRNGs without CI} shows the results on the batteries recalled above, indicating that almost all the PRNGs cannot pass all their tests. In other words, the statistical quality of these PRNGs cannot fulfill the up-to-date standards presented previously. We will show that the CIPRNG can solve this issue.
-
-To illustrate the effects of this CIPRNG in detail, experiments will be divided in three parts:
-\begin{enumerate}
- \item \textbf{Single CIPRNG}: The PRNGs involved in CI computing are of the same category.
- \item \textbf{Mixed CIPRNG}: Two different types of PRNGs are mixed during the chaotic iterations process.
- \item \textbf{Multiple CIPRNG}: The generator is obtained by repeating the composition of the iteration function as follows: $x^0\in \mathds{B}^{\mathsf{N}}$, and $\forall n\in \mathds{N}^{\ast },\forall i\in \llbracket1;\mathsf{N}\rrbracket,$
-\begin{equation}
-\begin{array}{l}
-x_i^n=\left\{
-\begin{array}{l}
-x_i^{n-1}~~~~~\text{if}~S^n\neq i \\
-\forall j\in \llbracket1;\mathsf{m}\rrbracket,f^m(x^{n-1})_{S^{nm+j}}~\text{if}~S^{nm+j}=i.\end{array} \right. \end{array}
-\end{equation}
-$m$ is called the \emph{functional power}.
-\end{enumerate}
-
-
-We have performed statistical analysis of each of the aforementioned CIPRNGs.
-The results are reproduced in Tables~\ref{NIST and DieHARD tests suite passing rate the for PRNGs without CI} and \ref{NIST and DieHARD tests suite passing rate the for single CIPRNGs}.
-The scores written in boldface indicate that all the tests have been passed successfully, whereas an asterisk ``*'' means that the considered passing rate has been improved.
+Table~\ref{NIST and DieHARD tests suite passing rate the for PRNGs without CI} shows the
+results on the two firsts batteries recalled above, indicating that all the PRNGs presented
+in the previous section
+cannot pass all these tests. In other words, the statistical quality of these PRNGs cannot
+fulfill the up-to-date standards presented previously. We have shown in~\cite{bfg12a:ip} that the use of chaotic
+iterations can solve this issue.
+%More precisely, to
+%illustrate the effects of chaotic iterations on these defective PRNGs, experiments have been divided in three parts~\cite{bfg12a:ip}:
+%\begin{enumerate}
+% \item \textbf{Single CIPRNG}: The PRNGs involved in CI computing are of the same category.
+% \item \textbf{Mixed CIPRNG}: Two different types of PRNGs are mixed during the chaotic iterations process.
+% \item \textbf{Multiple CIPRNG}: The generator is obtained by repeating the composition of the iteration function as follows: $x^0\in \mathds{B}^{\mathsf{N}}$, and $\forall n\in \mathds{N}^{\ast },\forall i\in \llbracket1;\mathsf{N}\rrbracket, x_i^n=$
+%\begin{equation}
+%\begin{array}{l}
+%\left\{
+%\begin{array}{l}
+%x_i^{n-1}~~~~~\text{if}~S^n\neq i \\
+%\forall j\in \llbracket1;\mathsf{m}\rrbracket,f^m(x^{n-1})_{S^{nm+j}}~\text{if}~S^{nm+j}=i.\end{array} \right. \end{array}
+%\end{equation}
+%$m$ is called the \emph{functional power}.
+%\end{enumerate}
+%
+The obtained results are reproduced in Table
+\ref{NIST and DieHARD tests suite passing rate the for single CIPRNGs}.
+The scores written in boldface indicate that all the tests have been passed successfully, whereas an
+asterisk ``*'' means that the considered passing rate has been improved.
+The improvements are obvious for both the ``Old CI'' and ``New CI'' generators.
+Concerning the ``Xor CI PRNG'', the speed improvement makes that statistical
+results are not as good as for the two other versions of these CIPRNGs.
-\subsubsection{Tests based on the Single CIPRNG}
\begin{table*}
\renewcommand{\arraystretch}{1.3}
\end{tabular}
\end{table*}
-The statistical tests results of the PRNGs using the single CIPRNG method are given in Table~\ref{NIST and DieHARD tests suite passing rate the for single CIPRNGs}.
-We can observe that, except for the Xor CIPRNG, all of the CIPRNGs have passed the 15 tests of the NIST battery and the 18 tests of the DieHARD one.
-Moreover, considering these scores, we can deduce that both the single Old CIPRNG and the single New CIPRNG are relatively steadier than the single Xor CIPRNG approach, when applying them to different PRNGs.
-However, the Xor CIPRNG is obviously the fastest approach to generate a CI random sequence, and it still improves the statistical properties relative to each generator taken alone, although the test values are not as good as desired.
-
-Therefore, all of these three ways are interesting, for different reasons, in the production of pseudorandom numbers and,
-on the whole, the single CIPRNG method can be considered to adapt to or improve all kinds of PRNGs.
-
-To have a realization of the Xor CIPRNG that can pass all the tests embedded into the NIST battery, the Xor CIPRNG with multiple functional powers are investigated in Section~\ref{Tests based on Multiple CIPRNG}.
-
-
-\subsubsection{Tests based on the Mixed CIPRNG}
-
-To compare the previous approach with the CIPRNG design that uses a Mixed CIPRNG, we have taken into account the same inputted generators than in the previous section.
-These inputted couples $(PRNG_1,PRNG_2)$ of PRNGs are used in the Mixed approach as follows:
-\begin{equation}
-\left\{
-\begin{array}{l}
-x^0 \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket, S \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket^\mathds{N} \\
-\forall n \in \mathds{N}^*, x^n = x^{n-1} \oplus PRNG_1\oplus PRNG_2,
-\end{array}
-\right.
-\label{equation Oplus}
-\end{equation}
-
-With this Mixed CIPRNG approach, both the Old CIPRNG and New CIPRNG continue to pass all the NIST and DieHARD suites.
-In addition, we can see that the PRNGs using a Xor CIPRNG approach can pass more tests than previously.
-The main reason of this success is that the Mixed Xor CIPRNG has a longer period.
-Indeed, let $n_{P}$ be the period of a PRNG $P$, then the period deduced from the single Xor CIPRNG approach is obviously equal to:
-\begin{equation}
-n_{SXORCI}=
-\left\{
-\begin{array}{ll}
-n_{P}&\text{if~}x^0=x^{n_{P}}\\
-2n_{P}&\text{if~}x^0\neq x^{n_{P}}.\\
-\end{array}
-\right.
-\label{equation Oplus}
-\end{equation}
-
-Let us now denote by $n_{P1}$ and $n_{P2}$ the periods of respectively the $PRNG_1$ and $PRNG_2$ generators, then the period of the Mixed Xor CIPRNG will be:
-\begin{equation}
-n_{XXORCI}=
-\left\{
-\begin{array}{ll}
-LCM(n_{P1},n_{P2})&\text{if~}x^0=x^{LCM(n_{P1},n_{P2})}\\
-2LCM(n_{P1},n_{P2})&\text{if~}x^0\neq x^{LCM(n_{P1},n_{P2})}.\\
-\end{array}
-\right.
-\label{equation Oplus}
-\end{equation}
-
-In Table~\ref{DieHARD fail mixex CIPRNG}, we only show the results for the Mixed CIPRNGs that cannot pass all DieHARD suites (the NIST tests are all passed). It demonstrates that Mixed Xor CIPRNG involving LCG, MRG, LCG2, LCG3, MRG2, or INV cannot pass the two following tests, namely the ``Matrix Rank 32x32'' and the ``COUNT-THE-1's'' tests contained into the DieHARD battery. Let us recall their definitions:
-
-\begin{itemize}
- \item \textbf{Matrix Rank 32x32.} A random 32x32 binary matrix is formed, each row having a 32-bit random vector. Its rank is an integer that ranges from 0 to 32. Ranks less than 29 must be rare, and their occurences must be pooled with those of rank 29. To achieve the test, ranks of 40,000 such random matrices are obtained, and a chisquare test is performed on counts for ranks 32,31,30 and for ranks $\leq29$.
-
- \item \textbf{COUNT-THE-1's TEST} Consider the file under test as a stream of bytes (four per 2 bit integer). Each byte can contain from 0 to 8 1's, with probabilities 1,8,28,56,70,56,28,8,1 over 256. Now let the stream of bytes provide a string of overlapping 5-letter words, each ``letter'' taking values A,B,C,D,E. The letters are determined by the number of 1's in a byte: 0,1, or 2 yield A, 3 yields B, 4 yields C, 5 yields D and 6,7, or 8 yield E. Thus we have a monkey at a typewriter hitting five keys with various probabilities (37,56,70,56,37 over 256). There are $5^5$ possible 5-letter words, and from a string of 256,000 (over-lapping) 5-letter words, counts are made on the frequencies for each word. The quadratic form in the weak inverse of the covariance matrix of the cell counts provides a chisquare test: Q5-Q4, the difference of the naive Pearson sums of $(OBS-EXP)^2/EXP$ on counts for 5- and 4-letter cell counts.
-\end{itemize}
-
-The reason of these fails is that the output of LCG, LCG2, LCG3, MRG, and MRG2 under the experiments are in 31-bit. Compare with the Single CIPRNG, using different PRNGs to build CIPRNG seems more efficient in improving random number quality (mixed Xor CI can 100\% pass NIST, but single cannot).
-
-\begin{table*}
-\renewcommand{\arraystretch}{1.3}
-\caption{Scores of mixed Xor CIPRNGs when considering the DieHARD battery}
-\label{DieHARD fail mixex CIPRNG}
-\centering
- \begin{tabular}{|l||c|c|c|c|c|c|}
- \hline
-\backslashbox{\textbf{$PRNG_1$}} {\textbf{$PRNG_0$}} & LCG & MRG & INV & LCG2 & LCG3 & MRG2 \\ \hline\hline
-LCG &\backslashbox{} {} &16/18&16/18 &16/18 &16/18 &16/18\\ \hline
-MRG &16/18 &\backslashbox{} {} &16/18&16/18 &16/18 &16/18\\ \hline
-INV &16/18 &16/18&\backslashbox{} {} &16/18 &16/18&16/18 \\ \hline
-LCG2 &16/18 &16/18 &16/18 &\backslashbox{} {} &16/18&16/18\\ \hline
-LCG3 &16/18 &16/18 &16/18&16/18&\backslashbox{} {} &16/18\\ \hline
-MRG2 &16/18 &16/18 &16/18&16/18 &16/18 &\backslashbox{} {} \\ \hline
-\end{tabular}
-\end{table*}
-
-\subsubsection{Tests based on the Multiple CIPRNG}
-\label{Tests based on Multiple CIPRNG}
-
-Until now, the combination of at most two input PRNGs has been investigated.
-We now regard the possibility to use a larger number of generators to improve the statistics of the generated pseudorandom numbers, leading to the multiple functional power approach.
-For the CIPRNGs which have already pass both the NIST and DieHARD suites with 2 inputted PRNGs (all the Old and New CIPRNGs, and some of the Xor CIPRNGs), it is not meaningful to consider their adaption of this multiple CIPRNG method, hence only the Multiple Xor CIPRNGs, having the following form, will be investigated.
-\begin{equation}
-\left\{
-\begin{array}{l}
-x^0 \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket, S \in \llbracket 0, 2^\mathsf{N}-1 \rrbracket^\mathds{N} \\
-\forall n \in \mathds{N}^*, x^n = x^{n-1} \oplus S^{nm}\oplus S^{nm+1}\ldots \oplus S^{nm+m-1} ,
-\end{array}
-\right.
-\label{equation Oplus}
-\end{equation}
-
-The question is now to determine the value of the threshold $m$ (the functional power) making the multiple CIPRNG being able to pass the whole NIST battery.
-Such a question is answered in Table~\ref{threshold}.
+We have then investigate in~\cite{bfg12a:ip} if it is possible to improve
+the statistical behavior of the Xor CI version by combining more than one
+$\oplus$ operation. Results are summarized in~\ref{threshold}, showing
+that rapid and perfect PRNGs, regarding the NIST and DieHARD batteries, can be obtained
+using chaotic iterations on defective generators.
\begin{table*}
\renewcommand{\arraystretch}{1.3}
-\caption{Functional power $m$ making it possible to pass the whole NIST battery}
+\caption{Number of $\oplus$ operations to pass the whole NIST and DieHARD batteries}
\label{threshold}
\centering
\begin{tabular}{|l||c|c|c|c|c|c|c|c|}
\end{tabular}
\end{table*}
-\subsubsection{Results Summary}
+Finally, the TestU01 battery as been launched on three well-known generators
+(a logistic map, a simple XORshift, and the cryptographically secure ISAAC,
+see Table~\ref{TestU011}). These results can be compared with
+Table~\ref{TestU01 for Old CI}, which gives the scores obtained by the
+Old CI PRNG that has received these generators.
-We can summarize the obtained results as follows.
-\begin{enumerate}
-\item The CIPRNG method is able to improve the statistical properties of a large variety of PRNGs.
-\item Using different PRNGs in the CIPRNG approach is better than considering several instances of one unique PRNG.
-\item The statistical quality of the outputs increases with the functional power $m$.
-\end{enumerate}
+Next subsection gives a concrete implementation of this Xor CI PRNG, which will
+new be simply called CIPRNG, or ``the proposed PRNG'', if this statement does not
+raise ambiguity.
\end{color}
-\section{Efficient PRNG based on Chaotic Iterations}
+\subsection{Efficient Implementation of a PRNG based on Chaotic Iterations}
\label{sec:efficient PRNG}
-
-Based on the proof presented in the previous section, it is now possible to
-improve the speed of the generator formerly presented in~\cite{bgw09:ip,guyeux10}.
-The first idea is to consider
-that the provided strategy is a pseudorandom Boolean vector obtained by a
-given PRNG.
-An iteration of the system is simply the bitwise exclusive or between
-the last computed state and the current strategy.
-Topological properties of disorder exhibited by chaotic
-iterations can be inherited by the inputted generator, we hope by doing so to
-obtain some statistical improvements while preserving speed.
-
+%
+%Based on the proof presented in the previous section, it is now possible to
+%improve the speed of the generator formerly presented in~\cite{bgw09:ip,guyeux10}.
+%The first idea is to consider
+%that the provided strategy is a pseudorandom Boolean vector obtained by a
+%given PRNG.
+%An iteration of the system is simply the bitwise exclusive or between
+%the last computed state and the current strategy.
+%Topological properties of disorder exhibited by chaotic
+%iterations can be inherited by the inputted generator, we hope by doing so to
+%obtain some statistical improvements while preserving speed.
+%
%%RAPH : j'ai viré tout ca
%% Let us give an example using 16-bits numbers, to clearly understand how the bitwise xor operations
%% are
-\lstset{language=C,caption={C code of the sequential PRNG based on chaotic iterations},label=algo:seqCIPRNG}
+\lstset{language=C,caption={C code of the sequential PRNG based on chaotic iterations},label={algo:seqCIPRNG}}
\begin{small}
\begin{lstlisting}
\begin{color}{red}
\subsection{Practical Security Evaluation}
+\label{sec:Practicak evaluation}
Suppose now that the PRNG will work during
$M=100$ time units, and that during this period,
