[freehaven-cvs] More tweaks

[nickm@seul.org (Nick Mathewson)]

Update of /home/freehaven/cvsroot/doc/e2e-traffic
In directory moria.mit.edu:/tmp/cvs-serv31090

Modified Files:
	e2e-traffic.tex 
Log Message:
More tweaks

Index: e2e-traffic.tex
===================================================================
RCS file: /home/freehaven/cvsroot/doc/e2e-traffic/e2e-traffic.tex,v
retrieving revision 1.53
retrieving revision 1.54
diff -u -d -r1.53 -r1.54
--- e2e-traffic.tex	3 May 2004 00:05:01 -0000	1.53
+++ e2e-traffic.tex	3 May 2004 00:10:22 -0000	1.54
@@ -736,7 +736,7 @@
 round, also chosen uniformly at random.  We ran 100 trial attacks for each
 chosen $\left<N,m,b\right>$ tuple.  Each attack was set to halt when the
 attacker has correctly identified Alice's recipients, or when 1,000,000
-rounds had passed.  (We imposed this cap on run time to keep our simulator
+rounds had passed.  (We imposed this cap to keep our simulator
 from getting stuck on hopeless cases.)
 
 %\begin{figure}[ht]
@@ -782,11 +782,11 @@
 
 We model the background as a graph of $N$ communicating parties, each of whom
 communicates with some of the others.  We build this graph according to the
-``scale-free'' model proposed in \cite{scale-emergence} and analyzed in
-\cite{scale-analysis}, which shares desirable properties with ``small-world''
-networks \cite{small-world}.  Scale-free networks share the ``six degrees of
+``scale-free'' model \cite{scale-emergence,scale-analysis}.
+Scale-free networks share the ``six degrees of
 separation property'' (for arbitrary values of six) of small-world
-networks, but also mimic the clustering and `organic' growth of real social
+networks \cite{small-world}, but also mimic the clustering and `organic'
+growth of real social
 networks, including citations in journals, co-stars in IMDB, and links in
 the WWW.
 %
@@ -805,22 +805,22 @@
 with equal frequency and choose recipients uniformly from among the people
 they know.
 
-Again, we simulated trial attacks for different values of $N$ (the number of
-recipients) and $m$ (the number of Alice's recipients).
-Instead of contributing one message per batch, however, Alice now
-contributes messages according to a geometric distribution with parameter
+We simulated trial attacks for different values of $N$ (number of
+recipients) and $m$ (number of Alice's recipients).
+Instead of sending one message per batch, however, Alice now
+sends messages according to a geometric distribution with parameter
 $P_{M}$ (such that Alice sends $n$ messages with probability $P_m(n) =
-(1-P_M)P_M^n$).  We also tried two methods for assigning Alice's
+(1-P_M)P_M^n$).  We tried two methods for assigning Alice's
 recipients: In the `uniform'
 model, Alice's recipients are chosen according to their connectedness (so
-that Alice, like everyone else, is likelier to know people who are already
-well-known), but Alice still sends to each chosen recipient with equal
-probability.  In the `weighted' model, not only are Alice's recipients
+that Alice, like everyone else, is likelier to know well-known people)
+but Alice still sends to her recipients with equal
+probability.  In the `weighted'  model, not only are Alice's recipients
 chosen according to their connectedness, but Alice also sends to them
-proportional to their connectedness.  We selected these
-models to examine the attack's effectiveness against users whose behavior is
-generated with the same model as other users' (U), and against users who mimic
-the background distribution (W).
+proportionally to their connectedness.  We selected these
+models to examine the attack's effectiveness against users who
+behave with the same model as other users', and against users who mimic
+the background distribution.
 
 The results are in Figure~\ref{fig2a}, along with the results for the
 original statistical disclosure attack as reference.  As expected, the attack
@@ -905,7 +905,7 @@
 
 This strategy slows the attack, but does not {\it necessarily} stop it.  As
 shown in Figure~\ref{fig5a}, independent geometric padding is most helpful
-when the underlying mix network has a higher variability in message delay to
+when the mix network has a higher variability in message delay to
 `spread' the padding between rounds.  Otherwise, Alice must send far more
 padding messages to confuse the attacker.
 

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