Merge pull request #35 from AAU-Dat/smallchanges

Smallchanges

Author: Maltesius

report/src/main.tex

@@ -13,6 +13,8 @@
 
 % Document
 \begin{document}
+    \input{sections/0-resume}
+    \newpage
     \maketitle
     \input{sections/00-abstract}
     \input{sections/01-introduction}

report/src/sections/0-resume.tex

@@ -0,0 +1,4 @@
+
+\begin{resume}
+    This is a placeholder for the resume
+\end{resume}

report/src/sections/02-background.tex

@@ -186,6 +186,6 @@ \subsection{Whisk}\label{subsec:related-work-whisk}
 \subsection{Problem definition}\label{subsec:problem-definition}
 The current proposal of Curdleproofs only works when the shuffle size of Whisk is set to a power of 2.
 The reason is that the underlying proofs,~\gls{dlipa} in~\gls{sameperm} and~\gls{samemsm}, need to fold recursively down to 1, by halving the size in every round.
-With the current shuffling size being 128, being able to choose the size more flexibly could lead to both performance and size gains.
+With the current shuffling size of 128, being able to choose the size more flexibly could lead to both performance and size gains.
 The problem we study in this article is therefore how to extend Curdleproofs to~$\ell$ values that are not a power of 2.
 

report/src/sections/appendix/02-thm1proof.tex

@@ -90,6 +90,15 @@ \section{Proof of Theorem 1}\label{sec:appendix-thm1proof}
     Furthermore, Curdleproofs uses the Fiat-Shamir transformation for its verifier challenges.
     So, the SIPA$(f)$, analogously CAAUrdleproofs, is a non-interactive random oracle argument having completeness and computational knowledge soundness as well.
 
+    Now we switch our focus to another argument, namely the~\gls{samemsm} argument.
+    As with the~\gls{dlipa},~\gls{samemsm} is also an~\gls{ipa}.
+    Hence, to work in CAAUrdleproofs, it also needs the optimizations used in the~\gls{dlipa}.
+
+    This means that the~\gls{samemsm} argument uses the Springproofs scheme function.
+    Also, it uses the new computation of $\mathbf{s}$, see~\autoref{lst:ipa-verifier-optimized}, used for coupling the correct challenges to each element in the vector.
+    Furthermore, Curdleproofs blinds the argument in the same way as the~\gls{dlipa}.
+    Hence, our argumentation of~\gls{hvzk}, knowledge-soundness and completeness follows from the above explaination of~\gls{dlipa}.
+
     From this, we can conclude that CAAUrdleproofs is a zero-knowledge argument of knowledge when shuffle size $|\ell|\geq8$.
 \end{proof}