[freehaven-cvs] tweaks, fixes, typos

[arma@seul.org (Roger Dingledine)]

Update of /home2/freehaven/cvsroot/doc/e2e-traffic
In directory moria.mit.edu:/tmp/cvs-serv26351

Modified Files:
	e2e-traffic.tex 
Log Message:
tweaks, fixes, typos


Index: e2e-traffic.tex
===================================================================
RCS file: /home2/freehaven/cvsroot/doc/e2e-traffic/e2e-traffic.tex,v
retrieving revision 1.20
retrieving revision 1.21
diff -u -d -r1.20 -r1.21
--- e2e-traffic.tex	24 Jan 2004 02:39:34 -0000	1.20
+++ e2e-traffic.tex	24 Jan 2004 03:45:07 -0000	1.21
@@ -29,7 +29,7 @@
 
 \begin{document}
 \title{Practical Traffic Analysis: \\
-       Extending and resisting statistical disclosure}
+       Extending and Resisting Statistical Disclosure}
 
 \author{Nick Mathewson and Roger Dingledine}
 \institute{The Free Haven Project\\
@@ -749,14 +749,14 @@
 \label{fig1}
 \end{figure}
 
-We present the results of our simulations in figure \ref{fig1}.  (We found
+We present the results of our simulations in Figure \ref{fig1}.  (We found
 that the time required for the attacker to learn Alice's last few recipients
 was highly variable, especially in later simulations, so we present instead
 the $90^{th}$ percentile of number of rounds required to $m-1$ of Alice's
 recipients, across 100 trials per data point.)  As expected, the attack
-becomes more effective when Alice's sends messages to a brader group of
+becomes more effective when Alice sends messages to a broader group of
 recipients (large $m$); when there are fewer recipients for Alice to hide
-hers among (small $N$); or when batch sizes are large (large $b$).
+hers among (small $N$); or when batch sizes are small (small $b$).
 
 \subsubsection{Complex sender behavior and unknown background traffic}
 % trial2
@@ -824,14 +824,14 @@
 \label{fig2b}
 \end{figure}
 
-The results are in figures \ref{fig2a} and \ref{fig2b}.  Lines that run off
+The results are in Figures \ref{fig2a} and \ref{fig2b}.  Lines that run off
 the top of the graph represent cases in which the attacks did not converge on
 $m-1$ of Alice's recipients within 1,000,000 rounds.  As expected, the attack
 succeeded fastest against the UU cases for equivalent values of
 $\left<N,m,b>\right>$, followed by BU and BB.  Also, Alice's message volume
 parameter $P_M$ had little effect on the attack for the range examined, other
 than to force the attacker to wait for a greater number of rounds to elapse
-before Alice has sent enought traffic.
+before Alice has sent enough traffic.
 
 \subsubsection{Attacking pool mixes and mix-nets}
 \label{subsec:sim-complex-mixes}
@@ -861,13 +861,13 @@
 \centering
 \mbox{\epsfig{angle=0,figure=graphs/fig34,width=4in}}
 \caption{Pool mixes and mix-nets: Rounds to guess $m-1$ recipients 
-         ($90^{th$} percentile of trial attacks)}
+         ($90^{th}$ percentile of trial attacks)}
 \label{fig34}
 \end{figure}
 
-To examine the effect of pool paramters, we fixed $m$ at $32$ and $N$ at
-$2^16$.  The results of these simulations are presented in figure
-\ref{fig34}.  From this, we note two interesting effects:  first,
+To examine the effect of pool parameters, we fixed $m$ at $32$ and $N$ at
+$2^16$.  The results of these simulations are presented in
+Figure~\ref{fig34}.  From this, we note two interesting effects:  first,
 pooling has the most effect when Alice has a very high traffic volume, and is
 only incrementally helpful when Alice has 
 

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