\documentclass{article}
\usepackage[margin=1in]{geometry}
\usepackage{color}
\usepackage{hyperref}
\usepackage[
    n,
    operators,
    advantage,
    sets,
    adversary,
    landau,
    probability,
    notions,    
    logic,
    ff,
    mm,
    primitives,
    events,
    complexity,
    asymptotics,
    keys]{cryptocode}
\title{Short Discrete Log Proof Changes for bounds on each coefficient}
\date{July 7th 2023}

\begin{document}

\maketitle

The original \href{https://eprint.iacr.org/2019/057}{short discrete log proof paper} used a fixed size for the bounds of each coefficient in the matrix that we are attempting to show knowledge of (the matrix $\mathbf{S}$ in the linear equation $\mathbf{AS} = \mathbf{T}$). It is possible to modify the algorithm such that a bound is specified on coefficients are different. This is useful in a few different ways; the two parties can easily agree on pieces of the polynomials that are not used (by defining the bound of zero), and additionally the bounds can be significantly lowered for a small performance increase on terms that are related to small random elements in encryption schemes.

Below is the algorithm as specified. It is mostly the same as the prior case, but any text in red has changed from the original paper. 

\begin{figure}
\centering

\pseudocodeblock{
  \textbf{Prover P} \< \< \textbf{Verifier V} \\[0.1\baselineskip][\hline]
   \< \< \\[-0.5\baselineskip]
  \text{Inputs:} \< \< \\
  \mathbf{A} \in \mathcal{R}_q^{n \times m}, \mathbf{S} \in \mathcal{S}_{\mathbf{B}}^{m \times k}, {\color{red} \mathbf{B} \in (\mathbb{N}^d)^{m \times k}} \< \< \mathbf{A}, \mathbf{T}, {\color{red} \mathbf{B}'}, b_1, b_2, l, \vec{g}, \vec{h}, u\\
  \mathbf{T} = \mathbf{A}\mathbf{S} \in \mathcal{R}_q^{n \times k} \< \< \\
  {\color{red} \mathbf{B}'_{mkd} = \lceil \log(\mathbf{B}_{mkd}) \rceil + 1 \text{ if } \mathbf{B}_{mkd} \neq 0 \text{ else } 0}\< \< \\
  {\color{red} b_1 = \max_k\left(\left\lceil \log \left(\sum_{m,d} \mathbf{B}_{mdk} + d ||f||_{\infty}\right) \right\rceil \right)} \< \< \\
  b_2 = \lceil \log(q) \rceil \< \< \\
  l = \sum {\color{red} \mathbf{B}'} + nk(2d - 1)b_1 + nk(d-1)b_2 \< \< \\
  \vec{g}, \vec{h} \in G^l, u \in G \< \< 
  \\[0.1\baselineskip][\hline]
   \< \< \\[-0.5\baselineskip]
   \mathbf{R}_2 = (\mathbf{T} - \mathbf{AS})/\mathbf{f} \text{ over } \mathbb{Z}_q[X] \< \< \\
   \mathbf{R}_1 = (\mathbf{T - AS - fR}_2)/q \text{ over } \mathbb{Z}[X] \\
   \vec{s} = \mathrm{Serialize}(\mathbf{S}) \in \mathbb{Z}^{mkd} \< \< \\
   \vec{r}_1 = \mathrm{Serialize}(\mathbf{R}_1) \in \mathbb{Z}^{nk(2d - 1)} \< \< \\
   \vec{r}_2 = \mathrm{Serialize}(\mathbf{R}_2) \in \mathbb{Z}^{nk(d-1)} \< \< \\
   {\color{red} \vec{b} = \mathrm{Serialize}(\mathbf{B'}) \in \mathbb{Z}^{mkd}} \< \< \\
   \vec{s}_1 = {\color{red} \mathrm{Binary}_{\vec{b}}(\vec{s})} \ || \ \mathrm{Binary}_{b_1}(\vec{r}_1) \ || \ \mathrm{Binary}_{b_2}(\vec{r}_2) \< \< \\
   \rho \sample \mathbb{Z}_p \< \< \\
   \vec{s}_2 = \vec{s}_1 + \vec{1} \in \mathbb{Z}_2^l \text{ (XOR)} \< \< \\
   w = \vec{g}^{\vec{s}_2}\vec{h}^{\vec{s}_1}u^{\rho} \< \sendmessageright*{w} \< \\
   \< \< \alpha \sample \mathbb{Z}_p^{\times}, \vec{\beta} \sample (\mathbb{Z}_p^{\times})^k, \vec{\gamma} \sample (\mathbb{Z}_p^{\times})^n \\
   \< \sendmessageleft*{\alpha, \vec{\beta}, \vec{\gamma}, \vec{\varphi}, \psi} \< \vec{\varphi} \sample (\mathbb{Z}_p^{\times})^l, \phi \sample \mathbb{Z}_p^{\times} \\
   \vec{g}' = \vec{g}^{\vec{\varphi}^{-1}} \< \< \vec{g}' = \vec{g}^{\vec{\varphi}^{-1}} \\
   \vec{v} = {\color{red} \mathrm{Serialize}(\mathrm{diag}(\mathbf{A}(\alpha)^T\vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_d) \vec{2}_{\vec{b}})} \< \< \vec{v} = {\color{red} \mathrm{Serialize}(\mathrm{diag}(\mathbf{A}(\alpha)^T\vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_d) \vec{2}_{\vec{b}})} \\
   \ \ \ \ \ \ || \ q \vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_{2d - 1} \otimes \vec{2}_{b_1} \< \< \ \ \ \ \ \ || \ q \vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_{2d - 1} \otimes \vec{2}_{b_1}  \\
   \ \ \ \ \ \ || \ \mathbf{f}(\alpha)\vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_{d - 1} \otimes \vec{2}_{b_2} \< \< \ \ \ \ \ \ || \ \mathbf{f}(\alpha)\vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_{d - 1} \otimes \vec{2}_{b_2} \\
   t = w(\vec{g})^{\vec{v} + \psi \vec{\varphi}}\vec{h}^{\psi} \< \< t = w(\vec{g})^{\vec{v} + \psi \vec{\varphi}}\vec{h}^{\psi} \\
   \vec{v}_1 = \vec{v} + \vec{\psi} \circ \vec{s}_2 ++ \varphi \vec{\psi} \< \< \\
   \vec{v}_2 = \vec{s}_1 + \varphi \vec{1} \< \< \\
   x = \langle \vec{v}_1, \vec{v}_2 \rangle \< \< x = \vec{\gamma}^T\mathbf{T}(\alpha)\vec{\beta} + \varphi \langle \vec{v}, \vec{1} \rangle + (\varphi + \varphi^2)\langle \vec{\psi}, \vec{1} \rangle \in \mathbb{Z}_p\\
  \\[0.1\baselineskip][\hline]
   \< \< \\[-0.5\baselineskip]
}
The parties run the zero-knowledge inner product proof $\vartriangleright\!\Pi_{\langle \cdot , \cdot \rangle}(\vec{g}', \vec{h}, u, t, x; \vec{v}_1, \vec{v}_2, \rho)$ and the verifier V accepts if they accept in $\vartriangleright\!\Pi_{\langle \cdot , \cdot \rangle}(.; .)$.

$\mathrm{Binary}_{\vec{z}}(\vec{y})$ does a binary expansion of each $y \in \vec{y}$ by the size $z \in \vec{z}$. $\vec{2}_{\vec{b}}$ is a jagged matrix of the power of 2 expansion for every $b \in \vec{b}$ (including expanding to an empty vector if $b = 0$); hence the line $\vec{v} = \ldots$ scales each vector in $\vec{2}_{b_i}$ by $(\mathbf{A}(\alpha)^T\vec{\gamma} \otimes \vec{\beta} \otimes \vec{\alpha}_d)_i$ and serializes the result.
\label{fig:sdlp_changes}
\end{figure}

\end{document}