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Fabrice Mouhartem 2018-04-10 10:32:28 +02:00
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sec-lattices.tex
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@ -82,9 +82,9 @@ In other words, it means that no polynomial time algorithms can solve those prob
\begin{definition}[The $\SIS$ and $\ISIS$ problem] \label{de:sis} \index{Lattices!Short Integer Solution} \index{Lattices!Inhomogeneous \SIS}
Let~$m,q,\beta$ be functions of~$n \in \mathbb{N}$.
The \textit{Short Integer Solution} problem $\SIS_{n,m,q,\beta}$ is, given~$\mathbf{A} \sample U(\Zq^{n \times m})$, find~$\mathbf{x} \in \Lambda_q^{\perp}(\mathbf{A})$ with~$0 < \|\mathbf{x}\| \leq \beta$.
The \textit{Short Integer Solution} problem $\SIS_{n,m,q,\beta}$ is, given~$\mathbf{A} \sample \U(\Zq^{n \times m})$, find~$\mathbf{x} \in \Lambda_q^{\perp}(\mathbf{A})$ with~$0 < \|\mathbf{x}\| \leq \beta$.
The \textit{Inhomogeneous Short Integer Solution}~$\ISIS_{n,m,q,\beta}$ problem is, given~$\mathbf{A} \sample U(\Zq^{n \times m})$ and $\mathbf u \in \Zq^n$, find~$\mathbf{x} \in \Lambda_q^{\mathbf u}(\mathbf A)$ with~$0 < \| \mathbf x \| \leq \beta$.
The \textit{Inhomogeneous Short Integer Solution}~$\ISIS_{n,m,q,\beta}$ problem is, given~$\mathbf{A} \sample \U(\Zq^{n \times m})$ and $\mathbf u \in \Zq^n$, find~$\mathbf{x} \in \Lambda_q^{\mathbf u}(\mathbf A)$ with~$0 < \| \mathbf x \| \leq \beta$.
\end{definition}
Evidences of the hardness of the $\SIS$ and $\ISIS$ assumptions are given by the following Lemma, which reduced these problems from $\SIVP$.
@ -96,8 +96,8 @@ Evidences of the hardness of the $\SIS$ and $\ISIS$ assumptions are given by the
\begin{definition}[The $\LWE$ problem] \label{de:lwe} \index{Lattices!Learning With Errors}
Let $n,m \geq 1$, $q \geq 2$, and let $\chi$ be a probability distribution on~$\mathbb{Z}$.
For $\mathbf{s} \in \mathbb{Z}_q^n$, let $A_{\mathbf{s}, \chi}$ be the distribution obtained by sampling $\mathbf{a} \hookleftarrow U(\mathbb{Z}_q^n)$ and $e \hookleftarrow \chi$, and outputting $(\mathbf{a}, \mathbf{a}^T\cdot\mathbf{s} + e) \in \mathbb{Z}_q^n \times \mathbb{Z}_q$.
The Learning With Errors problem $\mathsf{LWE}_{n,q,\chi}$ asks to distinguish~$m$ samples chosen according to $\mathcal{A}_{\mathbf{s},\chi}$ (for $\mathbf{s} \hookleftarrow U(\mathbb{Z}_q^n)$) and $m$ samples chosen according to $U(\mathbb{Z}_q^n \times \mathbb{Z}_q)$.
For $\mathbf{s} \in \mathbb{Z}_q^n$, let $A_{\mathbf{s}, \chi}$ be the distribution obtained by sampling $\mathbf{a} \hookleftarrow \U(\mathbb{Z}_q^n)$ and $e \hookleftarrow \chi$, and outputting $(\mathbf{a}, \mathbf{a}^T\cdot\mathbf{s} + e) \in \mathbb{Z}_q^n \times \mathbb{Z}_q$.
The Learning With Errors problem $\mathsf{LWE}_{n,q,\chi}$ asks to distinguish~$m$ samples chosen according to $\mathcal{A}_{\mathbf{s},\chi}$ (for $\mathbf{s} \hookleftarrow \U(\mathbb{Z}_q^n)$) and $m$ samples chosen according to $\U(\mathbb{Z}_q^n \times \mathbb{Z}_q)$.
\end{definition}
\begin{figure}
@ -148,7 +148,7 @@ The following Lemma states that it is possible to efficiently compute a uniform~
\begin{lemma}[{\cite[Th.~3.2]{AP09}}]
\label{le:TrapGen}
There exists a $\ppt$ algorithm $\TrapGen$ that takes as inputs $1^n$, $1^m$ and an integer~$q \geq 2$ with~$m \geq \Omega(n \log q)$, and outputs a matrix~$\mathbf{A} \in \ZZ_q^{n \times m}$ and a basis~$\mathbf{T}_{\mathbf{A}}$ of~$\Lambda_q^{\perp}(\mathbf{A})$ such that~$\mathbf{A}$ is within statistical distance~$2^{-\Omega(n)}$ to~$U(\ZZ_q^{n \times m})$, and~$\|\widetilde{\mathbf{T}_{\mathbf{A}}}\| \leq \bigO(\sqrt{n \log q})$.
There exists a $\ppt$ algorithm $\TrapGen$ that takes as inputs $1^n$, $1^m$ and an integer~$q \geq 2$ with~$m \geq \Omega(n \log q)$, and outputs a matrix~$\mathbf{A} \in \ZZ_q^{n \times m}$ and a basis~$\mathbf{T}_{\mathbf{A}}$ of~$\Lambda_q^{\perp}(\mathbf{A})$ such that~$\mathbf{A}$ is within statistical distance~$2^{-\Omega(n)}$ to~$\U(\ZZ_q^{n \times m})$, and~$\|\widetilde{\mathbf{T}_{\mathbf{A}}}\| \leq \bigO(\sqrt{n \log q})$.
\end{lemma}
\noindent Lemma~\ref{le:TrapGen} is often combined with the sampler from Lemma~\ref{le:GPV}. Micciancio and Peikert~\cite{MP12} proposed a more efficient approach for this combined task, which is to be be preferred in practice but, for the sake of simplicity, schemes are presented using $\TrapGen$ and $\GPVSample$ in this thesis.