X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/prng_gpu.git/blobdiff_plain/3139e5ae44965e6ea8f37d04f84687189d4b5c42..850c033d45e9af70be22cb2e0c76a9de99d23c17:/prng_gpu.tex?ds=inline diff --git a/prng_gpu.tex b/prng_gpu.tex index 8d76043..966dbaa 100644 --- a/prng_gpu.tex +++ b/prng_gpu.tex @@ -1,4 +1,5 @@ -\documentclass{article} +%\documentclass{article} +\documentclass[10pt,journal,letterpaper,compsoc]{IEEEtran} \usepackage[utf8]{inputenc} \usepackage[T1]{fontenc} \usepackage{fullpage} @@ -7,9 +8,11 @@ \usepackage{amscd} \usepackage{moreverb} \usepackage{commath} -\usepackage{algorithm2e} +\usepackage[ruled,vlined]{algorithm2e} \usepackage{listings} \usepackage[standard]{ntheorem} +\usepackage{algorithmic} +\usepackage{slashbox} % Pour mathds : les ensembles IR, IN, etc. \usepackage{dsfont} @@ -34,66 +37,260 @@ \newcommand{\alert}[1]{\begin{color}{blue}\textit{#1}\end{color}} -\title{Efficient generation of pseudo random numbers based on chaotic iterations on GPU} +\title{Efficient and Cryptographically Secure Generation of Chaotic Pseudorandom Numbers on GPU} \begin{document} -\author{Jacques M. Bahi, Rapha\"{e}l Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}} - -\maketitle +\author{Jacques M. Bahi, Rapha\"{e}l Couturier, Christophe +Guyeux, and Pierre-Cyrille Héam\thanks{Authors in alphabetic order}} + +\IEEEcompsoctitleabstractindextext{ \begin{abstract} -This is the abstract +In this paper we present a new pseudorandom number generator (PRNG) on +graphics processing units (GPU). This PRNG is based on the so-called chaotic iterations. It +is firstly proven to be chaotic according to the Devaney's formulation. We thus propose an efficient +implementation for GPU that successfully passes the {\it BigCrush} tests, deemed to be the hardest +battery of tests in TestU01. Experiments show that this PRNG can generate +about 20 billion of random numbers per second on Tesla C1060 and NVidia GTX280 +cards. +It is then established that, under reasonable assumptions, the proposed PRNG can be cryptographically +secure. +A chaotic version of the Blum-Goldwasser asymmetric key encryption scheme is finally proposed. + + \end{abstract} +} -\section{Introduction} +\maketitle + +\IEEEdisplaynotcompsoctitleabstractindextext +\IEEEpeerreviewmaketitle -Interet des itérations chaotiques pour générer des nombre alea\\ -Interet de générer des nombres alea sur GPU -\alert{RC, un petit state-of-the-art sur les PRNGs sur GPU ?} -... +\section{Introduction} + +Randomness is of importance in many fields such as scientific simulations or cryptography. +``Random numbers'' can mainly be generated either by a deterministic and reproducible algorithm +called a pseudorandom number generator (PRNG), or by a physical non-deterministic +process having all the characteristics of a random noise, called a truly random number +generator (TRNG). +In this paper, we focus on reproducible generators, useful for instance in +Monte-Carlo based simulators or in several cryptographic schemes. +These domains need PRNGs that are statistically irreproachable. +In some fields such as in numerical simulations, speed is a strong requirement +that is usually attained by using parallel architectures. In that case, +a recurrent problem is that a deflation of the statistical qualities is often +reported, when the parallelization of a good PRNG is realized. +This is why ad-hoc PRNGs for each possible architecture must be found to +achieve both speed and randomness. +On the other side, speed is not the main requirement in cryptography: the great +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. +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 +generator that is unpredictable, disordered, sensible to its seed, or in other word chaotic. +Their desire is to map a given chaotic dynamics into a sequence that seems random +and unassailable due to chaos. +However, the chaotic maps used as a pattern are defined in the real line +whereas computers deal with finite precision numbers. +This distortion leads to a deflation of both chaotic properties and speed. +Furthermore, authors of such chaotic generators often claim their PRNG +as secure due to their chaos properties, but there is no obvious relation +between chaos and security as it is understood in cryptography. +This is why the use of chaos for PRNG still remains marginal and disputable. + +The authors' opinion is that topological properties of disorder, as they are +properly defined in the mathematical theory of chaos, can reinforce the quality +of a PRNG. But they are not substitutable for security or statistical perfection. +Indeed, to the authors' mind, such properties can be useful in the two following situations. On the +one hand, a post-treatment based on a chaotic dynamical system can be applied +to a PRNG statistically deflective, in order to improve its statistical +properties. Such an improvement can be found, for instance, in~\cite{bgw09:ip,bcgr11:ip}. +On the other hand, chaos can be added to a fast, statistically perfect PRNG and/or a +cryptographically secure one, in case where chaos can be of interest, +\emph{only if these last properties are not lost during +the proposed post-treatment}. Such an assumption is behind this research work. +It leads to the attempts to define a +family of PRNGs that are chaotic while being fast and statistically perfect, +or cryptographically secure. +Let us finish this paragraph by noticing that, in this paper, +statistical perfection refers to the ability to pass the whole +{\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}. +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 +family, and that the sequence obtained after this post-treatment can pass the +NIST~\cite{Nist10}, DieHARD~\cite{Marsaglia1996}, and TestU01~\cite{LEcuyerS07} batteries of tests, even if the inputted generators +cannot. +The proposition of this paper is to improve widely the speed of the formerly +proposed generator, without any lack of chaos or statistical properties. +In particular, a version of this PRNG on graphics processing units (GPU) +is proposed. +Although GPU was initially designed to accelerate +the manipulation of images, they are nowadays commonly used in many scientific +applications. Therefore, it is important to be able to generate pseudorandom +numbers inside a GPU when a scientific application runs in it. This remark +motivates our proposal of a chaotic and statistically perfect PRNG for GPU. +Such device +allows us to generate almost 20 billion of pseudorandom numbers per second. +Furthermore, we show that the proposed post-treatment preserves the +cryptographical security of the inputted PRNG, when this last has such a +property. +Last, but not least, we propose a rewriting of the Blum-Goldwasser asymmetric +key encryption protocol by using the proposed method. + +The remainder of this paper is organized as follows. In Section~\ref{section:related + works} we review some GPU implementations of PRNGs. Section~\ref{section:BASIC + RECALLS} gives some basic recalls on the well-known Devaney's formulation of chaos, + 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 + gpu} describes and evaluates theoretically the GPU implementation. +Such generators are experimented in +Section~\ref{sec:experiments}. +We show in Section~\ref{sec:security analysis} that, if the inputted +generator is cryptographically secure, then it is the case too for the +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 +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} +\label{section:related works} + +Numerous research works on defining GPU based PRNGs have already been proposed in the +literature, so that exhaustivity is impossible. +This is why authors of this document only give reference to the most significant attempts +in this domain, from their subjective point of view. +The quantity of pseudorandom numbers generated per second is mentioned here +only when the information is given in the related work. +A million numbers per second will be simply written as +1MSample/s whereas a billion numbers per second is 1GSample/s. + +In \cite{Pang:2008:cec} a PRNG based on cellular automata is defined +with no requirement to an high precision integer arithmetic or to any bitwise +operations. Authors can generate about +3.2MSamples/s on a GeForce 7800 GTX GPU, which is quite an old card now. +However, there is neither a mention of statistical tests nor any proof of +chaos or cryptography in this document. + +In \cite{ZRKB10}, the authors propose different versions of efficient GPU PRNGs +based on Lagged Fibonacci or Hybrid Taus. They have used these +PRNGs for Langevin simulations of biomolecules fully implemented on +GPU. Performances of the GPU versions are far better than those obtained with a +CPU, and these PRNGs succeed to pass the {\it BigCrush} battery of TestU01. +However the evaluations of the proposed PRNGs are only statistical ones. + + +Authors of~\cite{conf/fpga/ThomasHL09} have studied the implementation of some +PRNGs on different computing architectures: CPU, field-programmable gate array +(FPGA), massively parallel processors, and GPU. This study is of interest, because +the performance of the same PRNGs on different architectures are compared. +FPGA appears as the fastest and the most +efficient architecture, providing the fastest number of generated pseudorandom numbers +per joule. +However, we notice that authors can ``only'' generate between 11 and 16GSamples/s +with a GTX 280 GPU, which should be compared with +the results presented in this document. +We can remark too that the PRNGs proposed in~\cite{conf/fpga/ThomasHL09} are only +able to pass the {\it Crush} battery, which is far easier than the {\it Big Crush} one. + +Lastly, Cuda has developed a library for the generation of pseudorandom numbers called +Curand~\cite{curand11}. Several PRNGs are implemented, among +other things +Xorwow~\cite{Marsaglia2003} and some variants of Sobol. The tests reported show that +their fastest version provides 15GSamples/s on the new Fermi C2050 card. +But their PRNGs cannot pass the whole TestU01 battery (only one test is failed). +\newline +\newline +We can finally remark that, to the best of our knowledge, no GPU implementation has been proven to be chaotic, and the cryptographically secure property has surprisingly never been considered. \section{Basic Recalls} \label{section:BASIC RECALLS} -This section is devoted to basic definitions and terminologies in the fields of topological chaos and chaotic iterations. -\subsection{Devaney's chaotic dynamical systems} -In the sequel $S^{n}$ denotes the $n^{th}$ term of a sequence $S$ and $V_{i}$ denotes the $i^{th}$ component of a vector $V$. $f^{k}=f\circ ...\circ f$ denotes the $k^{th}$ composition of a function $f$. Finally, the following notation is used: $\llbracket1;N\rrbracket=\{1,2,\hdots,N\}$. +This section is devoted to basic definitions and terminologies in the fields of +topological chaos and chaotic iterations. We assume the reader is familiar +with basic notions on topology (see for instance~\cite{Devaney}). + +\subsection{Devaney's Chaotic Dynamical Systems} -Consider a topological space $(\mathcal{X},\tau)$ and a continuous function $f : \mathcal{X} \rightarrow \mathcal{X}$. +In the sequel $S^{n}$ denotes the $n^{th}$ term of a sequence $S$ and $V_{i}$ +denotes the $i^{th}$ component of a vector $V$. $f^{k}=f\circ ...\circ f$ +is for the $k^{th}$ composition of a function $f$. Finally, the following +notation is used: $\llbracket1;N\rrbracket=\{1,2,\hdots,N\}$. + + +Consider a topological space $(\mathcal{X},\tau)$ and a continuous function $f : +\mathcal{X} \rightarrow \mathcal{X}$. \begin{definition} -$f$ is said to be \emph{topologically transitive} if, for any pair of open sets $U,V \subset \mathcal{X}$, there exists $k>0$ such that $f^k(U) \cap V \neq \varnothing$. +The function $f$ is said to be \emph{topologically transitive} if, for any pair of open sets +$U,V \subset \mathcal{X}$, there exists $k>0$ such that $f^k(U) \cap V \neq +\varnothing$. \end{definition} \begin{definition} -An element $x$ is a \emph{periodic point} for $f$ of period $n\in \mathds{N}^*$ if $f^{n}(x)=x$.% The set of periodic points of $f$ is denoted $Per(f).$ +An element $x$ is a \emph{periodic point} for $f$ of period $n\in \mathds{N}^*$ +if $f^{n}(x)=x$.% The set of periodic points of $f$ is denoted $Per(f).$ \end{definition} \begin{definition} -$f$ is said to be \emph{regular} on $(\mathcal{X}, \tau)$ if the set of periodic points for $f$ is dense in $\mathcal{X}$: for any point $x$ in $\mathcal{X}$, any neighborhood of $x$ contains at least one periodic point (without necessarily the same period). +$f$ is said to be \emph{regular} on $(\mathcal{X}, \tau)$ if the set of periodic +points for $f$ is dense in $\mathcal{X}$: for any point $x$ in $\mathcal{X}$, +any neighborhood of $x$ contains at least one periodic point (without +necessarily the same period). \end{definition} -\begin{definition} -$f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and topologically transitive. +\begin{definition}[Devaney's formulation of chaos~\cite{Devaney}] +The function $f$ is said to be \emph{chaotic} on $(\mathcal{X},\tau)$ if $f$ is regular and +topologically transitive. \end{definition} -The chaos property is strongly linked to the notion of ``sensitivity'', defined on a metric space $(\mathcal{X},d)$ by: +The chaos property is strongly linked to the notion of ``sensitivity'', defined +on a metric space $(\mathcal{X},d)$ by: \begin{definition} -\label{sensitivity} $f$ has \emph{sensitive dependence on initial conditions} -if there exists $\delta >0$ such that, for any $x\in \mathcal{X}$ and any neighborhood $V$ of $x$, there exist $y\in V$ and $n > 0$ such that $d\left(f^{n}(x), f^{n}(y)\right) >\delta $. +\label{sensitivity} The function $f$ has \emph{sensitive dependence on initial conditions} +if there exists $\delta >0$ such that, for any $x\in \mathcal{X}$ and any +neighborhood $V$ of $x$, there exist $y\in V$ and $n > 0$ such that +$d\left(f^{n}(x), f^{n}(y)\right) >\delta $. -$\delta$ is called the \emph{constant of sensitivity} of $f$. +The constant $\delta$ is called the \emph{constant of sensitivity} of $f$. \end{definition} -Indeed, Banks \emph{et al.} have proven in~\cite{Banks92} that when $f$ is chaotic and $(\mathcal{X}, d)$ is a metric space, then $f$ has the property of sensitive dependence on initial conditions (this property was formerly an element of the definition of chaos). To sum up, quoting Devaney in~\cite{Devaney}, a chaotic dynamical system ``is unpredictable because of the sensitive dependence on initial conditions. It cannot be broken down or simplified into two subsystems which do not interact because of topological transitivity. And in the midst of this random behavior, we nevertheless have an element of regularity''. Fundamentally different behaviors are consequently possible and occur in an unpredictable way. +Indeed, Banks \emph{et al.} have proven in~\cite{Banks92} that when $f$ is +chaotic and $(\mathcal{X}, d)$ is a metric space, then $f$ has the property of +sensitive dependence on initial conditions (this property was formerly an +element of the definition of chaos). To sum up, quoting Devaney +in~\cite{Devaney}, a chaotic dynamical system ``is unpredictable because of the +sensitive dependence on initial conditions. It cannot be broken down or +simplified into two subsystems which do not interact because of topological +transitivity. And in the midst of this random behavior, we nevertheless have an +element of regularity''. Fundamentally different behaviors are consequently +possible and occur in an unpredictable way. -\subsection{Chaotic iterations} +\subsection{Chaotic Iterations} \label{sec:chaotic iterations} @@ -103,23 +300,23 @@ Boolean \emph{state}. Having $\mathsf{N}$ Boolean values for these cells leads to the definition of a particular \emph{state of the system}. A sequence which elements belong to $\llbracket 1;\mathsf{N} \rrbracket $ is called a \emph{strategy}. The set of all strategies is -denoted by $\mathbb{S}.$ +denoted by $\llbracket 1, \mathsf{N} \rrbracket^\mathds{N}.$ \begin{definition} \label{Def:chaotic iterations} The set $\mathds{B}$ denoting $\{0,1\}$, let $f:\mathds{B}^{\mathsf{N}}\longrightarrow \mathds{B}^{\mathsf{N}}$ be -a function and $S\in \mathbb{S}$ be a strategy. The so-called +a function and $S\in \llbracket 1, \mathsf{N} \rrbracket^\mathds{N}$ be a ``strategy''. The so-called \emph{chaotic iterations} are defined by $x^0\in \mathds{B}^{\mathsf{N}}$ and -$$ +\begin{equation} \forall n\in \mathds{N}^{\ast }, \forall i\in \llbracket1;\mathsf{N}\rrbracket ,x_i^n=\left\{ \begin{array}{ll} x_i^{n-1} & \text{ if }S^n\neq i \\ \left(f(x^{n-1})\right)_{S^n} & \text{ if }S^n=i. \end{array}\right. -$$ +\end{equation} \end{definition} In other words, at the $n^{th}$ iteration, only the $S^{n}-$th cell is @@ -129,49 +326,59 @@ $\left(f(x^{n-1})\right)_{S^{n}}$ can be replaced by $\left(f(x^{k})\right)_{S^{n}}$, where $k0$. \medskip +\begin{itemize} +\item If $\varepsilon \geqslant 1$, we see that the distance +between $\left( G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is +strictly less than 1 after the $max(n_{0},n_{1})^{th}$ term (same state). +\medskip +\item If $\varepsilon <1$, then $\exists k\in \mathds{N},10^{-k}\geqslant +\varepsilon > 10^{-(k+1)}$. But $d_{s}(S^n,S)$ converges to 0, so +\begin{equation*} +\exists n_{2}\in \mathds{N},\forall n\geqslant +n_{2},d_{s}(S^n,S)<10^{-(k+2)}, +\end{equation*}% +thus after $n_{2}$, the $k+2$ first terms of $S^n$ and $S$ are equal. +\end{itemize} +\noindent As a consequence, the $k+1$ first entries of the strategies of $% +G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are the same ($G_{f}$ is a shift of strategies) and due to the definition of $d_{s}$, the floating part of +the distance between $(S^n,E^n)$ and $(S,E)$ is strictly less than $% +10^{-(k+1)}\leqslant \varepsilon $. + +In conclusion, +%%RAPH : ici j'ai rajouté une ligne +$ +\forall \varepsilon >0,$ $\exists N_{0}=max(n_{0},n_{1},n_{2})\in \mathds{N} +,$ $\forall n\geqslant N_{0},$ +$ d\left( G_{f}(S^n,E^n);G_{f}(S,E)\right) +\leqslant \varepsilon . +$ +$G_{f}$ is consequently continuous. \end{proof} - - -\subsection{A given system can always be claimed as non-chaotic} +It is now possible to study the topological behavior of the general chaotic +iterations. We will prove that, \begin{theorem} -Let $\mathcal{X}$ be a set and $f: \mathcal{X} \to \X$. -If $\X$ is infinite, then $\left( \X_{\tau_\infty}, f\right)$ is not chaotic (for the Devaney's formulation), where $\tau_\infty$ is the discrete topology. +\label{t:chaos des general} + The general chaotic iterations defined on Equation~\ref{general CIs} satisfy +the Devaney's property of chaos. \end{theorem} -\begin{proof} -Let us prove it by contradiction, assuming that $\left(\X_{\tau_\infty}, f\right)$ is both transitive and regular. +Let us firstly prove the following lemma. -Let $x \in \X$ and $\{x\}$ one of its neighborhood. This neighborhood must contain a periodic point for $f$, if we want that $\left(\X_{\tau_\infty}, f\right)$ is regular. Then $x$ must be a periodic point of $f$. +\begin{lemma}[Strong transitivity] +\label{strongTrans} + For all couples $X,Y \in \mathcal{X}$ and any neighborhood $V$ of $X$, we can +find $n \in \mathds{N}^*$ and $X' \in V$ such that $G^n(X')=Y$. +\end{lemma} -Let $I_x = \left\{ f^{(n)}(x), n \in \mathds{N}\right\}$. This set is finite because $x$ is periodic, and $\mathcal{X}$ is infinite, then $\exists y \in \mathcal{X}, y \notin I_x$. - -As $\left(\X_{\tau_\infty}, f\right)$ must be transitive, for all open nonempty sets $A$ and $B$, an integer $n$ must satisfy $f^{(n)}(A) \cap B \neq \varnothing$. However $\{x\}$ and $\{y\}$ are open sets and $y \notin I_x \Rightarrow \forall n, f^{(n)}\left( \{x\} \right) \cap \{y\} = \varnothing$. +\begin{proof} + Let $X=(S,E)$, $\varepsilon>0$, and $k_0 = \lfloor log_{10}(\varepsilon)+1 \rfloor$. +Any point $X'=(S',E')$ such that $E'=E$ and $\forall k \leqslant k_0, S'^k=S^k$, +are in the open ball $\mathcal{B}\left(X,\varepsilon\right)$. Let us define +$\check{X} = \left(\check{S},\check{E}\right)$, where $\check{X}= G^{k_0}(X)$. +We denote by $s\subset \llbracket 1; \mathsf{N} \rrbracket$ the set of coordinates +that are different between $\check{E}$ and the state of $Y$. Thus each point $X'$ of +the form $(S',E')$ where $E'=E$ and $S'$ starts with +$(S^0, S^1, \hdots, S^{k_0},s,\hdots)$, verifies the following properties: +\begin{itemize} + \item $X'$ is in $\mathcal{B}\left(X,\varepsilon\right)$, + \item the state of $G_f^{k_0+1}(X')$ is the state of $Y$. +\end{itemize} +Finally the point $\left(\left(S^0, S^1, \hdots, S^{k_0},s,s^0, s^1, \hdots\right); E\right)$, +where $(s^0,s^1, \hdots)$ is the strategy of $Y$, satisfies the properties +claimed in the lemma. \end{proof} +We can now prove the Theorem~\ref{t:chaos des general}. + +\begin{proof}[Theorem~\ref{t:chaos des general}] +Firstly, strong transitivity implies transitivity. + +Let $(S,E) \in\mathcal{X}$ and $\varepsilon >0$. To +prove that $G_f$ is regular, it is sufficient to prove that +there exists a strategy $\tilde S$ such that the distance between +$(\tilde S,E)$ and $(S,E)$ is less than $\varepsilon$, and such that +$(\tilde S,E)$ is a periodic point. + +Let $t_1=\lfloor-\log_{10}(\varepsilon)\rfloor$, and let $E'$ be the +configuration that we obtain from $(S,E)$ after $t_1$ iterations of +$G_f$. As $G_f$ is strongly transitive, there exists a strategy $S'$ +and $t_2\in\mathds{N}$ such +that $E$ is reached from $(S',E')$ after $t_2$ iterations of $G_f$. + +Consider the strategy $\tilde S$ that alternates the first $t_1$ terms +of $S$ and the first $t_2$ terms of $S'$: +%%RAPH : j'ai coupé la ligne en 2 +$$\tilde +S=(S_0,\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,$$$$\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,\dots).$$ It +is clear that $(\tilde S,E)$ is obtained from $(\tilde S,E)$ after +$t_1+t_2$ iterations of $G_f$. So $(\tilde S,E)$ is a periodic +point. Since $\tilde S_t=S_t$ for $t>32); + x = x^(unsigned int)(t3>>32); + x = x^(unsigned int)t2; + x = x^(unsigned int)(t1>>32); + x = x^(unsigned int)t3; + return x; +} +\end{lstlisting} +\end{small} -\begin{definition} -Let $x,y \in \big[ 0, 2^{10} \big[$. -$D$ denotes the function from $\big[ 0, 2^{10} \big[^2$ to $\mathds{R}^+$ defined by: $D(x,y) = D_e\left(e(x),e(y)\right) + D_s\left(s(x),s(y)\right)$, where: -\begin{center} -$\displaystyle{D_e(E,\check{E}) = \sum_{k=0}^\mathsf{9} \delta (E_k, \check{E}_k)}$, ~~and~ $\displaystyle{D_s(S,\check{S}) = \sum_{k = 1}^\infty \dfrac{|S^k-\check{S}^k|}{10^k}}$. -\end{center} -\end{definition} +In Listing~\ref{algo:seqCIPRNG} a sequential version of the proposed PRNG based +on chaotic iterations is presented. The xor operator is represented by +\textasciicircum. This function uses three classical 64-bits PRNGs, namely the +\texttt{xorshift}, the \texttt{xor128}, and the +\texttt{xorwow}~\cite{Marsaglia2003}. In the following, we call them ``xor-like +PRNGs''. As each xor-like PRNG uses 64-bits whereas our proposed generator +works with 32-bits, we use the command \texttt{(unsigned int)}, that selects the +32 least significant bits of a given integer, and the code \texttt{(unsigned + int)(t$>>$32)} in order to obtain the 32 most significant bits of \texttt{t}. -\begin{proposition} -$D$ is a distance on $\big[ 0, 2^{10} \big[$. -\end{proposition} +Thus producing a pseudorandom number needs 6 xor operations with 6 32-bits numbers +that are provided by 3 64-bits PRNGs. This version successfully passes the +stringent BigCrush battery of tests~\cite{LEcuyerS07}. -\begin{proof} -The three axioms defining a distance must be checked. -\begin{itemize} -\item $D \geqslant 0$, because everything is positive in its definition. If $D(x,y)=0$, then $D_e(x,y)=0$, so the integral parts of $x$ and $y$ are equal (they have the same binary decomposition). Additionally, $D_s(x,y) = 0$, then $\forall k \in \mathds{N}^*, s(x)^k = s(y)^k$. In other words, $x$ and $y$ have the same $k-$th decimal digit, $\forall k \in \mathds{N}^*$. And so $x=y$. -\item $D(x,y)=D(y,x)$. -\item Finally, the triangular inequality is obtained due to the fact that both $\delta$ and $\Delta(x,y)=|x-y|$ satisfy it. -\end{itemize} -\end{proof} +\section{Efficient PRNGs based on Chaotic Iterations on GPU} +\label{sec:efficient PRNG gpu} +In order to take benefits from the computing power of GPU, a program +needs to have independent blocks of threads that can be computed +simultaneously. In general, the larger the number of threads is, the +more local memory is used, and the less branching instructions are +used (if, while, ...), the better the performances on GPU is. +Obviously, having these requirements in mind, it is possible to build +a program similar to the one presented in Listing +\ref{algo:seqCIPRNG}, which computes pseudorandom numbers on GPU. To +do so, we must firstly recall that in the CUDA~\cite{Nvid10} +environment, threads have a local identifier called +\texttt{ThreadIdx}, which is relative to the block containing +them. Furthermore, in CUDA, parts of the code that are executed by the GPU, are +called {\it kernels}. -The convergence of sequences according to $D$ is not the same than the usual convergence related to the Euclidian metric. For instance, if $x^n \to x$ according to $D$, then necessarily the integral part of each $x^n$ is equal to the integral part of $x$ (at least after a given threshold), and the decimal part of $x^n$ corresponds to the one of $x$ ``as far as required''. -To illustrate this fact, a comparison between $D$ and the Euclidian distance is given Figure \ref{fig:comparaison de distances}. These illustrations show that $D$ is richer and more refined than the Euclidian distance, and thus is more precise. +\subsection{Naive Version for GPU} -\begin{figure}[t] -\begin{center} - \subfigure[Function $x \to dist(x;1,234) $ on the interval $(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien.pdf}}\quad - \subfigure[Function $x \to dist(x;3) $ on the interval $(0;5)$.]{\includegraphics[scale=.35]{DvsEuclidien2.pdf}} -\end{center} -\caption{Comparison between $D$ (in blue) and the Euclidian distane (in green).} -\label{fig:comparaison de distances} -\end{figure} + +It is possible to deduce from the CPU version a quite similar version adapted to GPU. +The simple principle consists in making each thread of the GPU computing the CPU version of our PRNG. +Of course, the three xor-like +PRNGs used in these computations must have different parameters. +In a given thread, these parameters are +randomly picked from another PRNGs. +The initialization stage is performed by the CPU. +To do it, the ISAAC PRNG~\cite{Jenkins96} is used to set all the +parameters embedded into each thread. + +The implementation of the three +xor-like PRNGs is straightforward when their parameters have been +allocated in the GPU memory. Each xor-like works with an internal +number $x$ that saves the last generated pseudorandom number. Additionally, the +implementation of the xor128, the xorshift, and the xorwow respectively require +4, 5, and 6 unsigned long as internal variables. +\begin{algorithm} +\begin{small} +\KwIn{InternalVarXorLikeArray: array with internal variables of the 3 xor-like +PRNGs in global memory\; +NumThreads: number of threads\;} +\KwOut{NewNb: array containing random numbers in global memory} +\If{threadIdx is concerned by the computation} { + retrieve data from InternalVarXorLikeArray[threadIdx] in local variables\; + \For{i=1 to n} { + compute a new PRNG as in Listing\ref{algo:seqCIPRNG}\; + store the new PRNG in NewNb[NumThreads*threadIdx+i]\; + } + store internal variables in InternalVarXorLikeArray[threadIdx]\; +} +\end{small} +\caption{Main kernel of the GPU ``naive'' version of the PRNG based on chaotic iterations} +\label{algo:gpu_kernel} +\end{algorithm} -\subsubsection{The semiconjugacy} -It is now possible to define a topological semiconjugacy between $\mathcal{X}$ and an interval of $\mathds{R}$: +Algorithm~\ref{algo:gpu_kernel} presents a naive implementation of the proposed PRNG on +GPU. Due to the available memory in the GPU and the number of threads +used simultaneously, the number of random numbers that a thread can generate +inside a kernel is limited (\emph{i.e.}, the variable \texttt{n} in +algorithm~\ref{algo:gpu_kernel}). For instance, if $100,000$ threads are used and +if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)}, +then the memory required to store all of the internals variables of both the xor-like +PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers} +and the pseudorandom numbers generated by our PRNG, is equal to $100,000\times ((4+5+6)\times +2+(1+100))=1,310,000$ 32-bits numbers, that is, approximately $52$Mb. -\begin{theorem} -Chaotic iterations on the phase space $\mathcal{X}$ are simple iterations on $\mathds{R}$, which is illustrated by the semiconjugacy of the diagram bellow: -\begin{equation*} -\begin{CD} -\left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right) @>G_{f_0}>> \left(~\mathcal{S}_{10} \times\mathds{B}^{10}, d~\right)\\ - @V{\varphi}VV @VV{\varphi}V\\ -\left( ~\big[ 0, 2^{10} \big[, D~\right) @>>g> \left(~\big[ 0, 2^{10} \big[, D~\right) -\end{CD} -\end{equation*} -\end{theorem} +This generator is able to pass the whole BigCrush battery of tests, for all +the versions that have been tested depending on their number of threads +(called \texttt{NumThreads} in our algorithm, tested up to $5$ million). -\begin{proof} -$\varphi$ has been constructed in order to be continuous and onto. -\end{proof} +\begin{remark} +The proposed algorithm has the advantage of manipulating independent +PRNGs, so this version is easily adaptable on a cluster of computers too. The only thing +to ensure is to use a single ISAAC PRNG. To achieve this requirement, a simple solution consists in +using a master node for the initialization. This master node computes the initial parameters +for all the different nodes involved in the computation. +\end{remark} -In other words, $\mathcal{X}$ is approximately equal to $\big[ 0, 2^\mathsf{N} \big[$. +\subsection{Improved Version for GPU} +As GPU cards using CUDA have shared memory between threads of the same block, it +is possible to use this feature in order to simplify the previous algorithm, +i.e., to use less than 3 xor-like PRNGs. The solution consists in computing only +one xor-like PRNG by thread, saving it into the shared memory, and then to use the results +of some other threads in the same block of threads. In order to define which +thread uses the result of which other one, we can use a combination array that +contains the indexes of all threads and for which a combination has been +performed. +In Algorithm~\ref{algo:gpu_kernel2}, two combination arrays are used. The +variable \texttt{offset} is computed using the value of +\texttt{combination\_size}. Then we can compute \texttt{o1} and \texttt{o2} +representing the indexes of the other threads whose results are used by the +current one. In this algorithm, we consider that a 32-bits xor-like PRNG has +been chosen. In practice, we use the xor128 proposed in~\cite{Marsaglia2003} in +which unsigned longs (64 bits) have been replaced by unsigned integers (32 +bits). +This version can also pass the whole {\it BigCrush} battery of tests. +\begin{algorithm} +\begin{small} +\KwIn{InternalVarXorLikeArray: array with internal variables of 1 xor-like PRNGs +in global memory\; +NumThreads: Number of threads\; +array\_comb1, array\_comb2: Arrays containing combinations of size combination\_size\;} +\KwOut{NewNb: array containing random numbers in global memory} +\If{threadId is concerned} { + retrieve data from InternalVarXorLikeArray[threadId] in local variables including shared memory and x\; + offset = threadIdx\%combination\_size\; + o1 = threadIdx-offset+array\_comb1[offset]\; + o2 = threadIdx-offset+array\_comb2[offset]\; + \For{i=1 to n} { + t=xor-like()\; + t=t\textasciicircum shmem[o1]\textasciicircum shmem[o2]\; + shared\_mem[threadId]=t\; + x = x\textasciicircum t\; -\subsection{Study of the chaotic iterations described as a real function} + store the new PRNG in NewNb[NumThreads*threadId+i]\; + } + store internal variables in InternalVarXorLikeArray[threadId]\; +} +\end{small} +\caption{Main kernel for the chaotic iterations based PRNG GPU efficient +version\label{IR}} +\label{algo:gpu_kernel2} +\end{algorithm} +\subsection{Theoretical Evaluation of the Improved Version} + +A run of Algorithm~\ref{algo:gpu_kernel2} consists in an operation ($x=x\oplus t$) having +the form of Equation~\ref{equation Oplus}, which is equivalent to the iterative +system of Eq.~\ref{eq:generalIC}. That is, an iteration of the general chaotic +iterations is realized between the last stored value $x$ of the thread and a strategy $t$ +(obtained by a bitwise exclusive or between a value provided by a xor-like() call +and two values previously obtained by two other threads). +To be certain that we are in the framework of Theorem~\ref{t:chaos des general}, +we must guarantee that this dynamical system iterates on the space +$\mathcal{X} = \mathcal{P}\left(\llbracket 1, \mathsf{N} \rrbracket\right)^\mathds{N}\times\mathds{B}^\mathsf{N}$. +The left term $x$ obviously belongs to $\mathds{B}^ \mathsf{N}$. +To prevent from any flaws of chaotic properties, we must check that the right +term (the last $t$), corresponding to the strategies, can possibly be equal to any +integer of $\llbracket 1, \mathsf{N} \rrbracket$. + +Such a result is obvious, as for the xor-like(), all the +integers belonging into its interval of definition can occur at each iteration, and thus the +last $t$ respects the requirement. Furthermore, it is possible to +prove by an immediate mathematical induction that, as the initial $x$ +is uniformly distributed (it is provided by a cryptographically secure PRNG), +the two other stored values shmem[o1] and shmem[o2] are uniformly distributed too, +(this is the induction hypothesis), and thus the next $x$ is finally uniformly distributed. + +Thus Algorithm~\ref{algo:gpu_kernel2} is a concrete realization of the general +chaotic iterations presented previously, and for this reason, it satisfies the +Devaney's formulation of a chaotic behavior. -\begin{figure}[t] +\section{Experiments} +\label{sec:experiments} + +Different experiments have been performed in order to measure the generation +speed. We have used a first computer equipped with a Tesla C1060 NVidia GPU card +and an +Intel Xeon E5530 cadenced at 2.40 GHz, and +a second computer equipped with a smaller CPU and a GeForce GTX 280. +All the +cards have 240 cores. + +In Figure~\ref{fig:time_xorlike_gpu} we compare the quantity of pseudorandom numbers +generated per second with various xor-like based PRNGs. In this figure, the optimized +versions use the {\it xor64} described in~\cite{Marsaglia2003}, whereas the naive versions +embed the three xor-like PRNGs described in Listing~\ref{algo:seqCIPRNG}. In +order to obtain the optimal performances, the storage of pseudorandom numbers +into the GPU memory has been removed. This step is time consuming and slows down the numbers +generation. Moreover this storage is completely +useless, in case of applications that consume the pseudorandom +numbers directly after generation. We can see that when the number of threads is greater +than approximately 30,000 and lower than 5 million, the number of pseudorandom numbers generated +per second is almost constant. With the naive version, this value ranges from 2.5 to +3GSamples/s. With the optimized version, it is approximately equal to +20GSamples/s. Finally we can remark that both GPU cards are quite similar, but in +practice, the Tesla C1060 has more memory than the GTX 280, and this memory +should be of better quality. +As a comparison, Listing~\ref{algo:seqCIPRNG} leads to the generation of about +138MSample/s when using one core of the Xeon E5530. + +\begin{figure}[htbp] \begin{center} - \subfigure[ICs on the interval $(0,9;1)$.]{\includegraphics[scale=.35]{ICs09a1.pdf}}\quad - \subfigure[ICs on the interval $(0,7;1)$.]{\includegraphics[scale=.35]{ICs07a95.pdf}}\\ - \subfigure[ICs on the interval $(0,5;1)$.]{\includegraphics[scale=.35]{ICs05a1.pdf}}\quad - \subfigure[ICs on the interval $(0;1)$]{\includegraphics[scale=.35]{ICs0a1.pdf}} + \includegraphics[width=\columnwidth]{curve_time_xorlike_gpu.pdf} \end{center} -\caption{Representation of the chaotic iterations.} -\label{fig:ICs} +\caption{Quantity of pseudorandom numbers generated per second with the xorlike-based PRNG} +\label{fig:time_xorlike_gpu} \end{figure} -\begin{figure}[t] -\begin{center} - \subfigure[ICs on the interval $(510;514)$.]{\includegraphics[scale=.35]{ICs510a514.pdf}}\quad - \subfigure[ICs on the interval $(1000;1008)$]{\includegraphics[scale=.35]{ICs1000a1008.pdf}} -\end{center} -\caption{ICs on small intervals.} -\label{fig:ICs2} -\end{figure} -\begin{figure}[t] +In Figure~\ref{fig:time_bbs_gpu} we highlight the performances of the optimized +BBS-based PRNG on GPU. On the Tesla C1060 we obtain approximately 700MSample/s +and on the GTX 280 about 670MSample/s, which is obviously slower than the +xorlike-based PRNG on GPU. However, we will show in the next sections that this +new PRNG has a strong level of security, which is necessarily paid by a speed +reduction. + +\begin{figure}[htbp] \begin{center} - \subfigure[ICs on the interval $(0;16)$.]{\includegraphics[scale=.3]{ICs0a16.pdf}}\quad - \subfigure[ICs on the interval $(40;70)$.]{\includegraphics[scale=.45]{ICs40a70.pdf}}\quad + \includegraphics[width=\columnwidth]{curve_time_bbs_gpu.pdf} \end{center} -\caption{General aspect of the chaotic iterations.} -\label{fig:ICs3} +\caption{Quantity of pseudorandom numbers generated per second using the BBS-based PRNG} +\label{fig:time_bbs_gpu} \end{figure} +All these experiments allow us to conclude that it is possible to +generate a very large quantity of pseudorandom numbers statistically perfect with the xor-like version. +To a certain extend, it is also the case with the secure BBS-based version, the speed deflation being +explained by the fact that the former version has ``only'' +chaotic properties and statistical perfection, whereas the latter is also cryptographically secure, +as it is shown in the next sections. -We have written a Python program to represent the chaotic iterations with the vectorial negation on the real line $\mathds{R}$. Various representations of these CIs are given in Figures \ref{fig:ICs}, \ref{fig:ICs2} and \ref{fig:ICs3}. It can be remarked that the function $g$ is a piecewise linear function: it is linear on each interval having the form $\left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, $n \in \llbracket 0;2^{10}\times 10 \rrbracket$ and its slope is equal to 10. Let us justify these claims: -\begin{proposition} -\label{Prop:derivabilite des ICs} -Chaotic iterations $g$ defined on $\mathds{R}$ have derivatives of all orders on $\big[ 0, 2^{10} \big[$, except on the 10241 points in $I$ defined by $\left\{ \dfrac{n}{10} ~\big/~ n \in \llbracket 0;2^{10}\times 10\rrbracket \right\}$. -Furthermore, on each interval of the form $\left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, with $n \in \llbracket 0;2^{10}\times 10 \rrbracket$, $g$ is a linear function, having a slope equal to 10: $\forall x \notin I, g'(x)=10$. -\end{proposition} -\begin{proof} -Let $I_n = \left[ \dfrac{n}{10}, \dfrac{n+1}{10}\right[$, with $n \in \llbracket 0;2^{10}\times 10 \rrbracket$. All the points of $I_n$ have the same integral prat $e$ and the same decimal part $s^0$: on the set $I_n$, functions $e(x)$ and $x \mapsto s(x)^0$ of Definition \ref{def:e et s} only depend on $n$. So all the images $g(x)$ of these points $x$: -\begin{itemize} -\item Have the same integral part, which is $e$, except probably the bit number $s^0$. In other words, this integer has approximately the same binary decomposition than $e$, the sole exception being the digit $s^0$ (this number is then either $e+2^{10-s^0}$ or $e-2^{10-s^0}$, depending on the parity of $s^0$, \emph{i.e.}, it is equal to $e+(-1)^{s^0}\times 2^{10-s^0}$). -\item A shift to the left has been applied to the decimal part $y$, losing by doing so the common first digit $s^0$. In other words, $y$ has been mapped into $10\times y - s^0$. -\end{itemize} -To sum up, the action of $g$ on the points of $I$ is as follows: first, make a multiplication by 10, and second, add the same constant to each term, which is $\dfrac{1}{10}\left(e+(-1)^{s^0}\times 2^{10-s^0}\right)-s^0$. -\end{proof} - -\begin{remark} -Finally, chaotic iterations are elements of the large family of functions that are both chaotic and piecewise linear (like the tent map). -\end{remark} +\section{Security Analysis} +\label{sec:security analysis} -\subsection{Comparison of the two metrics on $\big[ 0, 2^\mathsf{N} \big[$} - -The two propositions bellow allow to compare our two distances on $\big[ 0, 2^\mathsf{N} \big[$: - -\begin{proposition} -Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,\Delta~\right) \to \left(~\big[ 0, 2^\mathsf{N} \big[, D~\right)$ is not continuous. -\end{proposition} - -\begin{proof} -The sequence $x^n = 1,999\hdots 999$ constituted by $n$ 9 as decimal part, is such that: -\begin{itemize} -\item $\Delta (x^n,2) \to 0.$ -\item But $D(x^n,2) \geqslant 1$, then $D(x^n,2)$ does not converge to 0. -\end{itemize} -The sequential characterization of the continuity concludes the demonstration. -\end{proof} +In this section the concatenation of two strings $u$ and $v$ is classically +denoted by $uv$. +In a cryptographic context, a pseudorandom generator is a deterministic +algorithm $G$ transforming strings into strings and such that, for any +seed $s$ of length $m$, $G(s)$ (the output of $G$ on the input $s$) has size +$\ell_G(m)$ with $\ell_G(m)>m$. +The notion of {\it secure} PRNGs can now be defined as follows. +\begin{definition} +A cryptographic PRNG $G$ is secure if for any probabilistic polynomial time +algorithm $D$, for any positive polynomial $p$, and for all sufficiently +large $m$'s, +$$| \mathrm{Pr}[D(G(U_m))=1]-Pr[D(U_{\ell_G(m)})=1]|< \frac{1}{p(m)},$$ +where $U_r$ is the uniform distribution over $\{0,1\}^r$ and the +probabilities are taken over $U_m$, $U_{\ell_G(m)}$ as well as over the +internal coin tosses of $D$. +\end{definition} -A contrario: +Intuitively, it means that there is no polynomial time algorithm that can +distinguish a perfect uniform random generator from $G$ with a non +negligible probability. The interested reader is referred +to~\cite[chapter~3]{Goldreich} for more information. Note that it is +quite easily possible to change the function $\ell$ into any polynomial +function $\ell^\prime$ satisfying $\ell^\prime(m)>m)$~\cite[Chapter 3.3]{Goldreich}. + +The generation schema developed in (\ref{equation Oplus}) is based on a +pseudorandom generator. Let $H$ be a cryptographic PRNG. We may assume, +without loss of generality, that for any string $S_0$ of size $N$, the size +of $H(S_0)$ is $kN$, with $k>2$. It means that $\ell_H(N)=kN$. +Let $S_1,\ldots,S_k$ be the +strings of length $N$ such that $H(S_0)=S_1 \ldots S_k$ ($H(S_0)$ is the concatenation of +the $S_i$'s). The cryptographic PRNG $X$ defined in (\ref{equation Oplus}) +is the algorithm mapping any string of length $2N$ $x_0S_0$ into the string +$(x_0\oplus S_0 \oplus S_1)(x_0\oplus S_0 \oplus S_1\oplus S_2)\ldots +(x_o\bigoplus_{i=0}^{i=k}S_i)$. One in particular has $\ell_{X}(2N)=kN=\ell_H(N)$. +We claim now that if this PRNG is secure, +then the new one is secure too. \begin{proposition} -Id: $\left(~\big[ 0, 2^\mathsf{N} \big[,D~\right) \to \left(~\big[ 0, 2^\mathsf{N} \big[, \Delta ~\right)$ is a continuous fonction. +\label{cryptopreuve} +If $H$ is a secure cryptographic PRNG, then $X$ is a secure cryptographic +PRNG too. \end{proposition} \begin{proof} -If $D(x^n,x) \to 0$, then $D_e(x^n,x) = 0$ at least for $n$ larger than a given threshold, because $D_e$ only returns integers. So, after this threshold, the integral parts of all the $x^n$ are equal to the integral part of $x$. - -Additionally, $D_s(x^n, x) \to 0$, then $\forall k \in \mathds{N}^*, \exists N_k \in \mathds{N}, n \geqslant N_k \Rightarrow D_s(x^n,x) \leqslant 10^{-k}$. This means that for all $k$, an index $N_k$ can be found such that, $\forall n \geqslant N_k$, all the $x^n$ have the same $k$ firsts digits, which are the digits of $x$. We can deduce the convergence $\Delta(x^n,x) \to 0$, and thus the result. -\end{proof} - -The conclusion of these propositions is that the proposed metric is more precise than the Euclidian distance, that is: - -\begin{corollary} -$D$ is finer than the Euclidian distance $\Delta$. -\end{corollary} - -This corollary can be reformulated as follows: - -\begin{itemize} -\item The topology produced by $\Delta$ is a subset of the topology produced by $D$. -\item $D$ has more open sets than $\Delta$. -\item It is harder to converge for the topology $\tau_D$ inherited by $D$, than to converge with the one inherited by $\Delta$, which is denoted here by $\tau_\Delta$. -\end{itemize} - - -\subsection{Chaos of the chaotic iterations on $\mathds{R}$} -\label{chpt:Chaos des itérations chaotiques sur R} +The proposition is proved by contraposition. Assume that $X$ is not +secure. By Definition, there exists a polynomial time probabilistic +algorithm $D$, a positive polynomial $p$, such that for all $k_0$ there exists +$N\geq \frac{k_0}{2}$ satisfying +$$| \mathrm{Pr}[D(X(U_{2N}))=1]-\mathrm{Pr}[D(U_{kN}=1]|\geq \frac{1}{p(2N)}.$$ +We describe a new probabilistic algorithm $D^\prime$ on an input $w$ of size +$kN$: +\begin{enumerate} +\item Decompose $w$ into $w=w_1\ldots w_{k}$, where each $w_i$ has size $N$. +\item Pick a string $y$ of size $N$ uniformly at random. +\item Compute $z=(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y + \bigoplus_{i=1}^{i=k} w_i).$ +\item Return $D(z)$. +\end{enumerate} + + +Consider for each $y\in \mathbb{B}^{kN}$ the function $\varphi_{y}$ +from $\mathbb{B}^{kN}$ into $\mathbb{B}^{kN}$ mapping $w=w_1\ldots w_k$ +(each $w_i$ has length $N$) to +$(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y + \bigoplus_{i=1}^{i=k_1} w_i).$ By construction, one has for every $w$, +\begin{equation}\label{PCH-1} +D^\prime(w)=D(\varphi_y(w)), +\end{equation} +where $y$ is randomly generated. +Moreover, for each $y$, $\varphi_{y}$ is injective: if +$(y\oplus w_1)(y\oplus w_1\oplus w_2)\ldots (y\bigoplus_{i=1}^{i=k_1} +w_i)=(y\oplus w_1^\prime)(y\oplus w_1^\prime\oplus w_2^\prime)\ldots +(y\bigoplus_{i=1}^{i=k} w_i^\prime)$, then for every $1\leq j\leq k$, +$y\bigoplus_{i=1}^{i=j} w_i^\prime=y\bigoplus_{i=1}^{i=j} w_i$. It follows, +by a direct induction, that $w_i=w_i^\prime$. Furthermore, since $\mathbb{B}^{kN}$ +is finite, each $\varphi_y$ is bijective. Therefore, and using (\ref{PCH-1}), +one has +$\mathrm{Pr}[D^\prime(U_{kN})=1]=\mathrm{Pr}[D(\varphi_y(U_{kN}))=1]$ and, +therefore, +\begin{equation}\label{PCH-2} +\mathrm{Pr}[D^\prime(U_{kN})=1]=\mathrm{Pr}[D(U_{kN})=1]. +\end{equation} +Now, using (\ref{PCH-1}) again, one has for every $x$, +\begin{equation}\label{PCH-3} +D^\prime(H(x))=D(\varphi_y(H(x))), +\end{equation} +where $y$ is randomly generated. By construction, $\varphi_y(H(x))=X(yx)$, +thus +\begin{equation}%\label{PCH-3} %%RAPH : j'ai viré ce label qui existe déjà, il est 3 ligne avant +D^\prime(H(x))=D(yx), +\end{equation} +where $y$ is randomly generated. +It follows that +\begin{equation}\label{PCH-4} +\mathrm{Pr}[D^\prime(H(U_{N}))=1]=\mathrm{Pr}[D(U_{2N})=1]. +\end{equation} + From (\ref{PCH-2}) and (\ref{PCH-4}), one can deduce that +there exists a polynomial time probabilistic +algorithm $D^\prime$, a positive polynomial $p$, such that for all $k_0$ there exists +$N\geq \frac{k_0}{2}$ satisfying +$$| \mathrm{Pr}[D(H(U_{N}))=1]-\mathrm{Pr}[D(U_{kN}=1]|\geq \frac{1}{p(2N)},$$ +proving that $H$ is not secure, which is a contradiction. +\end{proof} -\subsubsection{Chaos according to Devaney} -We have recalled previously that the chaotic iterations $\left(\Go, \mathcal{X}_d\right)$ are chaotic according to the formulation of Devaney. We can deduce that they are chaotic on $\mathds{R}$ too, when considering the order topology, because: +\section{Cryptographical Applications} + +\subsection{A Cryptographically Secure PRNG for GPU} +\label{sec:CSGPU} + +It is possible to build a cryptographically secure PRNG based on the previous +algorithm (Algorithm~\ref{algo:gpu_kernel2}). Due to Proposition~\ref{cryptopreuve}, +it simply consists in replacing +the {\it xor-like} PRNG by a cryptographically secure one. +We have chosen the Blum Blum Shub generator~\cite{BBS} (usually denoted by BBS) having the form: +$$x_{n+1}=x_n^2~ mod~ M$$ where $M$ is the product of two prime numbers (these +prime numbers need to be congruent to 3 modulus 4). BBS is known to be +very slow and only usable for cryptographic applications. + + +The modulus operation is the most time consuming operation for current +GPU cards. So in order to obtain quite reasonable performances, it is +required to use only modulus on 32-bits integer numbers. Consequently +$x_n^2$ need to be lesser than $2^{32}$, and thus the number $M$ must be +lesser than $2^{16}$. So in practice we can choose prime numbers around +256 that are congruent to 3 modulus 4. With 32-bits numbers, only the +4 least significant bits of $x_n$ can be chosen (the maximum number of +indistinguishable bits is lesser than or equals to +$log_2(log_2(M))$). In other words, to generate a 32-bits number, we need to use +8 times the BBS algorithm with possibly different combinations of $M$. This +approach is not sufficient to be able to pass all the tests of TestU01, +as small values of $M$ for the BBS lead to + small periods. So, in order to add randomness we have proceeded with +the followings modifications. \begin{itemize} -\item $\left(\Go, \mathcal{X}_d\right)$ and $\left(g, \big[ 0, 2^{10} \big[_D\right)$ are semiconjugate by $\varphi$, -\item Then $\left(g, \big[ 0, 2^{10} \big[_D\right)$ is a system chaotic according to Devaney, because the semiconjugacy preserve this character. -\item But the topology generated by $D$ is finer than the topology generated by the Euclidian distance $\Delta$ -- which is the order topology. -\item According to Theorem \ref{Th:chaos et finesse}, we can deduce that the chaotic iterations $g$ are indeed chaotic, as defined by Devaney, for the order topology on $\mathds{R}$. +\item +Firstly, we define 16 arrangement arrays instead of 2 (as described in +Algorithm \ref{algo:gpu_kernel2}), but only 2 of them are used at each call of +the PRNG kernels. In practice, the selection of combination +arrays to be used is different for all the threads. It is determined +by using the three last bits of two internal variables used by BBS. +%This approach adds more randomness. +In Algorithm~\ref{algo:bbs_gpu}, +character \& is for the bitwise AND. Thus using \&7 with a number +gives the last 3 bits, thus providing a number between 0 and 7. +\item +Secondly, after the generation of the 8 BBS numbers for each thread, we +have a 32-bits number whose period is possibly quite small. So +to add randomness, we generate 4 more BBS numbers to +shift the 32-bits numbers, and add up to 6 new bits. This improvement is +described in Algorithm~\ref{algo:bbs_gpu}. In practice, the last 2 bits +of the first new BBS number are used to make a left shift of at most +3 bits. The last 3 bits of the second new BBS number are added to the +strategy whatever the value of the first left shift. The third and the +fourth new BBS numbers are used similarly to apply a new left shift +and add 3 new bits. +\item +Finally, as we use 8 BBS numbers for each thread, the storage of these +numbers at the end of the kernel is performed using a rotation. So, +internal variable for BBS number 1 is stored in place 2, internal +variable for BBS number 2 is stored in place 3, ..., and finally, internal +variable for BBS number 8 is stored in place 1. \end{itemize} -This result can be formulated as follows. - -\begin{theorem} -\label{th:IC et topologie de l'ordre} -The chaotic iterations $g$ on $\mathds{R}$ are chaotic according to the Devaney's formulation, when $\mathds{R}$ has his usual topology, which is the order topology. -\end{theorem} - -Indeed this result is weaker than the theorem establishing the chaos for the finer topology $d$. However the Theorem \ref{th:IC et topologie de l'ordre} still remains important. Indeed, we have studied in our previous works a set different from the usual set of study ($\mathcal{X}$ instead of $\mathds{R}$), in order to be as close as possible from the computer: the properties of disorder proved theoretically will then be preserved when computing. However, we could wonder whether this change does not lead to a disorder of a lower quality. In other words, have we replaced a situation of a good disorder lost when computing, to another situation of a disorder preserved but of bad quality. Theorem \ref{th:IC et topologie de l'ordre} prove exactly the contrary. - - - +\begin{algorithm} +\begin{small} +\KwIn{InternalVarBBSArray: array with internal variables of the 8 BBS +in global memory\; +NumThreads: Number of threads\; +array\_comb: 2D Arrays containing 16 combinations (in first dimension) of size combination\_size (in second dimension)\; +array\_shift[4]=\{0,1,3,7\}\; +} -\section{Efficient prng based on chaotic iterations} +\KwOut{NewNb: array containing random numbers in global memory} +\If{threadId is concerned} { + retrieve data from InternalVarBBSArray[threadId] in local variables including shared memory and x\; + we consider that bbs1 ... bbs8 represent the internal states of the 8 BBS numbers\; + offset = threadIdx\%combination\_size\; + o1 = threadIdx-offset+array\_comb[bbs1\&7][offset]\; + o2 = threadIdx-offset+array\_comb[8+bbs2\&7][offset]\; + \For{i=1 to n} { + t$<<$=4\; + t|=BBS1(bbs1)\&15\; + ...\; + t$<<$=4\; + t|=BBS8(bbs8)\&15\; + \tcp{two new shifts} + shift=BBS3(bbs3)\&3\; + t$<<$=shift\; + t|=BBS1(bbs1)\&array\_shift[shift]\; + shift=BBS7(bbs7)\&3\; + t$<<$=shift\; + t|=BBS2(bbs2)\&array\_shift[shift]\; + t=t\textasciicircum shmem[o1]\textasciicircum shmem[o2]\; + shared\_mem[threadId]=t\; + x = x\textasciicircum t\; -In order to implement efficiently a PRNG based on chaotic iterations it is -possible to improve previous works [ref]. One solution consists in considering -that the strategy used contains all the bits for which the negation is -achieved out. Then in order to apply the negation on these bits we can simply -apply the xor operator between the current number and the strategy. In -order to obtain the strategy we also use a classical PRNG. + store the new PRNG in NewNb[NumThreads*threadId+i]\; + } + store internal variables in InternalVarXorLikeArray[threadId] using a rotation\; +} +\end{small} +\caption{main kernel for the BBS based PRNG GPU} +\label{algo:bbs_gpu} +\end{algorithm} -Here is an example with 16-bits numbers showing how the bit operations are -applied. Suppose that $x$ and the strategy $S^i$ are defined in binary mode. -Then the following table shows the result of $x$ xor $S^i$. +In Algorithm~\ref{algo:bbs_gpu}, $n$ is for the quantity of random numbers that +a thread has to generate. The operation t<<=4 performs a left shift of 4 bits +on the variable $t$ and stores the result in $t$, and $BBS1(bbs1)\&15$ selects +the last four bits of the result of $BBS1$. Thus an operation of the form +$t<<=4; t|=BBS1(bbs1)\&15\;$ realizes in $t$ a left shift of 4 bits, and then +puts the 4 last bits of $BBS1(bbs1)$ in the four last positions of $t$. Let us +remark that the initialization $t$ is not a necessity as we fill it 4 bits by 4 +bits, until having obtained 32-bits. The two last new shifts are realized in +order to enlarge the small periods of the BBS used here, to introduce a kind of +variability. In these operations, we make twice a left shift of $t$ of \emph{at + most} 3 bits, represented by \texttt{shift} in the algorithm, and we put +\emph{exactly} the \texttt{shift} last bits from a BBS into the \texttt{shift} +last bits of $t$. For this, an array named \texttt{array\_shift}, containing the +correspondence between the shift and the number obtained with \texttt{shift} 1 +to make the \texttt{and} operation is used. For example, with a left shift of 0, +we make an and operation with 0, with a left shift of 3, we make an and +operation with 7 (represented by 111 in binary mode). + +It should be noticed that this generator has once more the form $x^{n+1} = x^n \oplus S^n$, +where $S^n$ is referred in this algorithm as $t$: each iteration of this +PRNG ends with $x = x \wedge t$. This $S^n$ is only constituted +by secure bits produced by the BBS generator, and thus, due to +Proposition~\ref{cryptopreuve}, the resulted PRNG is cryptographically +secure. + + + +\begin{color}{red} +\subsection{Practical Security Evaluation} + +Suppose now that the PRNG will work during +$M=100$ time units, and that during this period, +an attacker can realize $10^{12}$ clock cycles. +We thus wonder whether, during the PRNG's +lifetime, the attacker can distinguish this +sequence from truly random one, with a probability +greater than $\varepsilon = 0.2$. +We consider that $N$ has 900 bits. + +The random process is the BBS generator, which +is cryptographically secure. More precisely, it +is $(T,\varepsilon)-$secure: no +$(T,\varepsilon)-$distinguishing attack can be +successfully realized on this PRNG, if~\cite{Fischlin} $$ -\begin{array}{|cc|cccccccccccccccc|} -\hline -x &=&1&0&1&1&1&0&1&0&1&0&0&1&0&0&1&0\\ -\hline -S^i &=&0&1&1&0&0&1&1&0&1&1&1&0&0&1&1&1\\ -\hline -x \oplus S^i&=&1&1&0&1&1&1&0&0&0&1&1&1&0&1&0&1\\ -\hline - -\hline - \end{array} +T \leqslant \dfrac{L(N)}{6 N (log_2(N))\varepsilon^{-2}M^2}-2^7 N \varepsilon^{-2} M^2 log_2 (8 N \varepsilon^{-1}M) $$ +where $M$ is the length of the output ($M=100$ in +our example), and $L(N)$ is equal to +$$ +2.8\times 10^{-3} exp \left(1.9229 \times (N ~ln(2)^\frac{1}{3}) \times ln(N~ln 2)^\frac{2}{3}\right) +$$ +is the number of clock cycles to factor a $N-$bit +integer. -%% \begin{figure}[htbp] -%% \begin{center} -%% \fbox{ -%% \begin{minipage}{14cm} -%% unsigned int CIprng() \{\\ -%% static unsigned int x = 123123123;\\ -%% unsigned long t1 = xorshift();\\ -%% unsigned long t2 = xor128();\\ -%% unsigned long t3 = xorwow();\\ -%% x = x\textasciicircum (unsigned int)t1;\\ -%% x = x\textasciicircum (unsigned int)(t2$>>$32);\\ -%% x = x\textasciicircum (unsigned int)(t3$>>$32);\\ -%% x = x\textasciicircum (unsigned int)t2;\\ -%% x = x\textasciicircum (unsigned int)(t1$>>$32);\\ -%% x = x\textasciicircum (unsigned int)t3;\\ -%% return x;\\ -%% \} -%% \end{minipage} -%% } -%% \end{center} -%% \caption{sequential Chaotic Iteration PRNG} -%% \label{algo:seqCIprng} -%% \end{figure} - - - -\lstset{language=C,caption={C code of the sequential chaotic iterations based PRNG},label=algo:seqCIprng} -\begin{lstlisting} -unsigned int CIprng() { - static unsigned int x = 123123123; - unsigned long t1 = xorshift(); - unsigned long t2 = xor128(); - unsigned long t3 = xorwow(); - x = x^(unsigned int)t1; - x = x^(unsigned int)(t2>>32); - x = x^(unsigned int)(t3>>32); - x = x^(unsigned int)t2; - x = x^(unsigned int)(t1>>32); - x = x^(unsigned int)t3; - return x; -} -\end{lstlisting} - - - - - -In listing~\ref{algo:seqCIprng} a sequential version of our chaotic iterations -based PRNG is presented. The xor operator is represented by \textasciicircum. This function uses three classical 64-bits PRNG: the -\texttt{xorshift}, the \texttt{xor128} and the \texttt{xorwow}. In the -following, we call them xor-like PRNGSs. These three PRNGs are presented -in~\cite{Marsaglia2003}. As each xor-like PRNG used works with 64-bits and as our PRNG -works with 32-bits, the use of \texttt{(unsigned int)} selects the 32 least -significant bits whereas \texttt{(unsigned int)(t3$>>$32)} selects the 32 most -significants bits of the variable \texttt{t}. So to produce a random number -realizes 6 xor operations with 6 32-bits numbers produced by 3 64-bits PRNG. -This version successes the BigCrush of the TestU01 battery [P. L’ecuyer and - R. Simard. Testu01]. +A direct numerical application shows that this attacker +cannot achieve its $(10^{12},0.2)$ distinguishing +attack in that context. -\section{Efficient prng based on chaotic iterations on GPU} +\end{color} -In order to benefit from computing power of GPU, a program needs to define -independent blocks of threads which can be computed simultaneously. In general, -the larger the number of threads is, the more local memory is used and the less -branching instructions are used (if, while, ...), the better performance is -obtained on GPU. So with algorithm \ref{algo:seqCIprng} presented in the -previous section, it is possible to build a similar program which computes PRNG -on GPU. +\subsection{Toward a Cryptographically Secure and Chaotic Asymmetric Cryptosystem} +\label{Blum-Goldwasser} +We finish this research work by giving some thoughts about the use of +the proposed PRNG in an asymmetric cryptosystem. +This first approach will be further investigated in a future work. +\subsubsection{Recalls of the Blum-Goldwasser Probabilistic Cryptosystem} -\subsection{Naive version} +The Blum-Goldwasser cryptosystem is a cryptographically secure asymmetric key encryption algorithm +proposed in 1984~\cite{Blum:1985:EPP:19478.19501}. The encryption algorithm +implements a XOR-based stream cipher using the BBS PRNG, in order to generate +the keystream. Decryption is done by obtaining the initial seed thanks to +the final state of the BBS generator and the secret key, thus leading to the + reconstruction of the keystream. -From the CPU version, it is possible to obtain a quite similar version for GPU. -The principe consists in assigning the computation of a PRNG as in sequential to -each thread of the GPU. Of course, it is essential that the three xor-like -PRNGs used for our computation have different parameters. So we chose them -randomly with another PRNG. As the initialisation is performed by the CPU, we -have chosen to use the ISAAC PRNG [ref] to initalize all the parameters for the -GPU version of our PRNG. The implementation of the three xor-like PRNGs is -straightforward as soon as their parameters have been allocated in the GPU -memory. Each xor-like PRNGs used works with an internal number $x$ which keeps -the last generated random numbers. Other internal variables are also used by the -xor-like PRNGs. More precisely, the implementation of the xor128, the xorshift -and the xorwow respectively require 4, 5 and 6 unsigned long as internal -variables. +The key generation consists in generating two prime numbers $(p,q)$, +randomly and independently of each other, that are + congruent to 3 mod 4, and to compute the modulus $N=pq$. +The public key is $N$, whereas the secret key is the factorization $(p,q)$. -\begin{algorithm} -\KwIn{InternalVarXorLikeArray: array with internal variables of the 3 xor-like PRNGs in global memory\; -NumThreads: Number of threads\;} -\KwOut{NewNb: array containing random numbers in global memory} -\If{threadId is concerned} { - retrieve data from InternalVarXorLikeArray[threadId] in local variables\; - \For{i=1 to n} { - compute a new PRNG as in Listing\ref{algo:seqCIprng}\; - store the new PRNG in NewNb[NumThreads*threadId+i]\; - } - store internal variables in InternalVarXorLikeArray[threadId]\; -} +Suppose Bob wishes to send a string $m=(m_0, \dots, m_{L-1})$ of $L$ bits to Alice: +\begin{enumerate} +\item Bob picks an integer $r$ randomly in the interval $\llbracket 1,N\rrbracket$ and computes $x_0 = r^2~mod~N$. +\item He uses the BBS to generate the keystream of $L$ pseudorandom bits $(b_0, \dots, b_{L-1})$, as follows. For $i=0$ to $L-1$, +\begin{itemize} +\item $i=0$. +\item While $i \leqslant L-1$: +\begin{itemize} +\item Set $b_i$ equal to the least-significant\footnote{As signaled previously, BBS can securely output up to $\mathsf{N} = \lfloor log(log(N)) \rfloor$ of the least-significant bits of $x_i$ during each round.} bit of $x_i$, +\item $i=i+1$, +\item $x_i = (x_{i-1})^2~mod~N.$ +\end{itemize} +\end{itemize} +\item The ciphertext is computed by XORing the plaintext bits $m$ with the keystream: $ c = (c_0, \dots, c_{L-1}) = m \oplus b$. This ciphertext is $[c, y]$, where $y=x_{0}^{2^{L}}~mod~N.$ +\end{enumerate} -\caption{main kernel for the chaotic iterations based PRNG GPU version} -\label{algo:gpu_kernel} -\end{algorithm} -According to the available memory in the GPU and the number of threads used -simultenaously, the number of random numbers that a thread can generate inside a -kernel is limited, i.e. the variable \texttt{n} in -algorithm~\ref{algo:gpu_kernel}. For example, if $100,000$ threads are used and -if $n=100$\footnote{in fact, we need to add the initial seed (a 32-bits number)} -then the memory required to store internals variables of xor-like -PRNGs\footnote{we multiply this number by $2$ in order to count 32-bits numbers} -and random number of our PRNG is equals to $100,000\times ((4+5+6)\times -2+(1+100))=1,310,000$ 32-bits numbers, i.e. about $52$Mb. +When Alice receives $\left[(c_0, \dots, c_{L-1}), y\right]$, she can recover $m$ as follows: +\begin{enumerate} +\item Using the secret key $(p,q)$, she computes $r_p = y^{((p+1)/4)^{L}}~mod~p$ and $r_q = y^{((q+1)/4)^{L}}~mod~q$. +\item The initial seed can be obtained using the following procedure: $x_0=q(q^{-1}~{mod}~p)r_p + p(p^{-1}~{mod}~q)r_q~{mod}~N$. +\item She recomputes the bit-vector $b$ by using BBS and $x_0$. +\item Alice finally computes the plaintext by XORing the keystream with the ciphertext: $ m = c \oplus b$. +\end{enumerate} -All the tests performed to pass the BigCrush of TestU01 succeeded. Different -number of threads have been tested upto $10$ millions. -\begin{remark} -Algorithm~\ref{algo:gpu_kernel} has the advantage to manipulate independent -PRNGs, so this version is easily usable on a cluster of computer. The only thing -to ensure is to use a single ISAAC PRNG. For this, a simple solution consists in -using a master node for the initialization which computes the initial parameters -for all the differents nodes involves in the computation. -\end{remark} +\subsubsection{Proposal of a new Asymmetric Cryptosystem Adapted from Blum-Goldwasser} -\subsection{Version more suited to GPU} +We propose to adapt the Blum-Goldwasser protocol as follows. +Let $\mathsf{N} = \lfloor log(log(N)) \rfloor$ be the number of bits that can +be obtained securely with the BBS generator using the public key $N$ of Alice. +Alice will pick randomly $S^0$ in $\llbracket 0, 2^{\mathsf{N}-1}\rrbracket$ too, and +her new public key will be $(S^0, N)$. -As GPU offers shared memory mechanism between threads of the same block, it is -possible to use this in order to simplify the previous algorithm, i.e. using -less than 3 xor-like PRNGs. The solution consists in +To encrypt his message, Bob will compute +%%RAPH : ici, j'ai mis un simple $ +%\begin{equation} +$c = \left(m_0 \oplus (b_0 \oplus S^0), m_1 \oplus (b_0 \oplus b_1 \oplus S^0), \hdots, \right.$ +$ \left. m_{L-1} \oplus (b_0 \oplus b_1 \hdots \oplus b_{L-1} \oplus S^0) \right)$ +%%\end{equation} +instead of $\left(m_0 \oplus b_0, m_1 \oplus b_1, \hdots, m_{L-1} \oplus b_{L-1} \right)$. - threads of the same block compute a random -number and uses other random numbers of +The same decryption stage as in Blum-Goldwasser leads to the sequence +$\left(m_0 \oplus S^0, m_1 \oplus S^0, \hdots, m_{L-1} \oplus S^0 \right)$. +Thus, with a simple use of $S^0$, Alice can obtain the plaintext. +By doing so, the proposed generator is used in place of BBS, leading to +the inheritance of all the properties presented in this paper. -\section{Experiments} +\section{Conclusion} -Differents experiments have been performed in order to measure the generation speed. +In this paper, a formerly proposed PRNG based on chaotic iterations +has been generalized to improve its speed. It has been proven to be +chaotic according to Devaney. +Efficient implementations on GPU using xor-like PRNGs as input generators +have shown that a very large quantity of pseudorandom numbers can be generated per second (about +20Gsamples/s), and that these proposed PRNGs succeed to pass the hardest battery in TestU01, +namely the BigCrush. +Furthermore, we have shown that when the inputted generator is cryptographically +secure, then it is the case too for the PRNG we propose, thus leading to +the possibility to develop fast and secure PRNGs using the GPU architecture. +\begin{color}{red} An improvement of the Blum-Goldwasser cryptosystem, making it +behaves chaotically, has finally been proposed. \end{color} -First of all we have compared the time to generate X random numbers with both the CPU version and the GPU version. +In future work we plan to extend this research, building a parallel PRNG for clusters or +grid computing. Topological properties of the various proposed generators will be investigated, +and the use of other categories of PRNGs as input will be studied too. The improvement +of Blum-Goldwasser will be deepened. Finally, we +will try to enlarge the quantity of pseudorandom numbers generated per second either +in a simulation context or in a cryptographic one. -Faire une courbe du nombre de random en fonction du nombre de threads, éventuellement en fonction du nombres de threads par bloc. -\section{Conclusion} -\bibliographystyle{plain} +\bibliographystyle{plain} \bibliography{mabase} \end{document}