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. However, in an equivalent formulation, he or she should not be
+sequence. 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
BigCrush} successfully and all $p-$values are at least once inside
[0.01, 0.99].
Chaos, for its part, refers to the well-established definition of a
-chaotic dynamical system proposed by Devaney~\cite{Devaney}.
+chaotic dynamical system defined 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
statistical behavior). Experiments are also provided using BBS as the initial
random generator. The generation speed is significantly weaker.
Note also that an original qualitative comparison between topological chaotic
-properties and statistical test is also proposed.
+properties and statistical tests is also proposed.
%improvement related to the chaotic iteration based post-treatment, for
%our previously released PRNGs and a new efficient
%implementation on CPU.
- Section~\ref{sec:efficient PRNG
- gpu} describes and evaluates theoretically new effective versions of
+ Section~\ref{sec:efficient PRNG} %{sec:efficient PRNG
+% gpu}
+ describes and evaluates theoretically new effective versions of
our pseudorandom generators, in particular with a GPU implementation.
Such generators are experimented in
Section~\ref{sec:experiments}.
leading thus to a new PRNG that
should improve the statistical properties of each
generator taken alone.
-Furthermore, the generator obtained in this way possesses various chaos properties that none of the generators used as present input.
+Furthermore, the generator obtained in this way possesses various chaos properties that none of the generators used as input present.
\end{algorithmic}
\end{algorithm}
+
+We have shown in~\cite{bfg12a:ip} that the use of chaotic iterations
+implies an improvement of the statistical properties for all the
+inputted defective generators we have investigated.
+For instance, when considering the TestU01 battery with its 588 tests, we obtained 261
+failures for a PRNG based on the logistic map alone, and
+this number of failures falls below 138 in the Old CI(Logistic,Logistic) generator.
+In the XORshift case (146 failures when considering it alone), the results are more amazing,
+as the chaotic iterations post-treatment makes it fails only 8 tests.
+Further investigations have been systematically realized in \cite{bfg12a:ip}
+using a large set of inputted defective PRNGs, the three most used batteries of
+tests (DieHARD, NIST, and TestU01), and for all the versions of generators we have proposed.
+In all situations, an obvious improvement of the statistical behavior has
+been obtained, reinforcing the impression that chaos leads to statistical
+enhancement~\cite{bfg12a:ip}.
+
\subsection{Improving the Speed of the Former Generator}
Instead of updating only one cell at each iteration, we now propose to choose a
\section{Toward Efficiency and Improvement for CI PRNG}
+\label{sec:efficient PRNG}
\subsection{First 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}.
