[freehaven-cvs] a few fixes and notes

[arma@seul.org (Roger Dingledine)]

Update of /home/freehaven/cvsroot/doc/e2e-traffic
In directory moria.mit.edu:/home2/arma/work/freehaven/doc/e2e-traffic

Modified Files:
	e2e-traffic.bib e2e-traffic.tex 
Log Message:
a few fixes and notes


Index: e2e-traffic.bib
===================================================================
RCS file: /home/freehaven/cvsroot/doc/e2e-traffic/e2e-traffic.bib,v
retrieving revision 1.5
retrieving revision 1.6
diff -u -d -r1.5 -r1.6
--- e2e-traffic.bib	22 Jan 2004 03:50:14 -0000	1.5
+++ e2e-traffic.bib	22 Jan 2004 04:34:46 -0000	1.6
@@ -78,7 +78,7 @@
   year = {2003},
   month = {March},
   editor = {Roger Dingledine},
-  publisher = {SprRinger-Verlag, LNCS 2760},
+  publisher = {Springer-Verlag, LNCS 2760},
   www_ps_gz_url = {http://www.esat.kuleuven.ac.be/~cdiaz/papers/DS03.ps.gz},
 }
 

Index: e2e-traffic.tex
===================================================================
RCS file: /home/freehaven/cvsroot/doc/e2e-traffic/e2e-traffic.tex,v
retrieving revision 1.17
retrieving revision 1.18
diff -u -d -r1.17 -r1.18
--- e2e-traffic.tex	22 Jan 2004 03:50:14 -0000	1.17
+++ e2e-traffic.tex	22 Jan 2004 04:34:46 -0000	1.18
@@ -731,10 +731,10 @@
 \subsubsection{The original statistical disclosure attack}
 %trial1
 First, we simulated Danezis's original statistical disclosure attack,
-choosing different values for $N$ (the number of recipients), $m$ (the number
+varying the parameters $N$ (the number of recipients), $m$ (the number
 of Alice's recipients), and $b$ (the batch size).  The simulated ``Alice''
 sends a single message every round to one of her recipients, chosen uniformly
-at random.  The simulated background sends to $b-1$ addition recipients per
+at random.  The simulated background sends to $b-1$ additional recipients per
 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 had correctly identified Alice's recipients, or until 1,000,000
@@ -748,7 +748,7 @@
 The next simulation examines the consequences of a more complex model for
 background traffic, and of several related models for Alice's behavior.
 
-We model the background as a graph $N$ communicating parties, each of whom
+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''
@@ -758,7 +758,9 @@
 networks, including citations in journals, co-actors in IMDB, and links in
 the WWW.
 
-%Should I include this?
+%Should I include this? -NM
+%If we run low on space, I would take out the first half of it, and
+% merge the second half with the above paragraph. -RD
 The scale-free model works as follows: we begin with a small number of
 interconnected vertices (in our case, 2).  Then, we iteratively add vertices
 to our graph, connecting each new vertex to every older vertex $i$ with
@@ -786,26 +788,26 @@
 in the `background/background' (BB) model, not only are Alice's recipients
 chosen according to their connectedness, but Alice also sends to them with
 probability proportional to their connectedness.  We selected these three
-models to examine the attack's effectiveness against users whose behavior was
-unrelated to other users' behavior (UU), users whose behavior was generated
-with the same model ather users' (BU), and users who mimiced the background
-distribution (BB).
+models to examine the attack's effectiveness against users whose behavior
+is unrelated to other users' behavior (UU), users whose behavior is
+generated with the same model as other users' (BU), and users who mimic
+the background distribution (BB).
 
 (Describe results)
 
 \subsubsection{Attacking pool mixes and mix-nets}
 \label{subsec:sim-complex-mixes}
 %trials 3,4
-Pooling slows an attacker by increasing the number of possible output
-messages corresponding to each input message.  To simulate an attack against
+Pooling slows an attacker by increasing the number of output messages
+that can correspond to each input message.  To simulate an attack against
 pool mixes and mix-nets, we abstract away the actual pooling rule used by the
 network, and instead assume that the network has reached a steady state, so
 that each mix sends the same fraction of the messages in its pool every
 round.  We also assume that all senders choose paths of exactly the same
 length.
 
-Unlike in the simulations above, `rounds' are now determined---not by a batch
-mix receiving a fixed number $b$ of messages---but by the passage of a fixed
+Unlike in the simulations above, `rounds' are now determined not by a batch
+mix receiving a fixed number $b$ of messages, but by the passage of a fixed
 interval of time.  Thus, the number of messages sent by the background is no
 longer a fixed $b-n_a$ (where $n_a$ is the number of messages Alice sends),
 but now follows a normal distribution with mean $B$ (and standard deviation
@@ -825,9 +827,10 @@
 Several proposals exist for using dummy messages to frustrate traffic
 analysis.  Although several of them have been examined in the context of
 low-latency systems \cite{defensive-dropping}, little work has been done to
-examine their effectiveness against long term intersection attacks.
+examine their effectiveness against long-term intersection attacks.
 
-First, we chose to restrict our examination (due to time limitations) to the
+%hm. tense switches between present and past. -rd
+First, we chose to restrict our examination (due to time constraints) to the
 effects of dummy messages in several cases of the pool-mix/mix-net simulation
 above.  Because we are interested in learning how well dummies thwart
 analysis, we chose cases where, in the absence of dummies, the attacker had
@@ -900,7 +903,7 @@
 
 \section*{Acknowledgments}
 %Thanks also to Gerald Britton, Geoffrey Goodell, Pete St. Onge, Peter
-%Palfrader, Al Riddoch Mike Taylor, and Dave Turner for letting us run our
+%Palfrader, Al Riddoch, Mike Taylor, and Dave Turner for letting us run our
 %simulations on their computers.
 
 %======================================================================

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