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\begin{document}
\title{Tor and circumvention: lessons learned\\
(Abstract to go with invited talk)}
\author{Roger Dingledine}
\institute{The Tor Project}
\maketitle

Tor is a free-software anonymizing overlay network that helps people
around the world use the Internet in safety. Tor's 2500 volunteer relays
carry almost 10Gb/s of traffic for several hundred thousand users each day.

While many in the
research community know Tor as the primary fielded system in the anonymous
communications literature~\cite{dingledine2004tor}, Tor has also played a
central role in recent
research on \emph{blocking resistance}. That is, even if an anonymity
system provides
great anonymity, a government censor can render it moot by simply
blocking the relays. In recent years we streamlined Tor's
network communications to look more like ordinary SSL, and we introduced
``bridge relays'' that are harder for an attacker to find and block than
Tor's public relays~\cite{tor-blocking}.

Tor played a key role in several Middle Eastern countries in early
2011. In this talk I'll walk the audience through how Iran used
its Nokia DPI boxes to filter SSL flows that used Tor's original Diffie-Hellman
parameter $p$; the surge in Tor traffic when Egypt blocked Facebook and
the flatline when they unplugged the net; the continued bad news for
Libya's Internet; and an intriguing trend in Saudi Arabia. I'll also
cover current trends in China and Tunisia (not pictured).

\begin{figure}[h]
\begin{minipage}{0.495\textwidth}
\includegraphics[width=\linewidth]{direct-users-2011-04-01-300-ir-2010-12-01.png}
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\begin{minipage}{0.495\textwidth}
\includegraphics[width=\linewidth]{direct-users-2011-04-01-300-eg-2010-12-01.png}
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\includegraphics[width=\linewidth]{direct-users-2011-04-01-300-ly-2010-12-01.png}
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\begin{minipage}{0.495\textwidth}
\includegraphics[width=\linewidth]{direct-users-2011-04-01-300-sa-2010-09-01.png}
\end{minipage}
\caption{Estimates of daily Tor clients connecting from each country}
\end{figure}

The data for these user graphs, along with historical Tor network
data and ongoing performance statistics, are all available at
https://metrics.torproject.org/. Our WECSR'10
paper~\cite{wecsr10measuring-tor} explains our
aggregation techniques and why we think they're safe---we'd love for you
to show us that we're wrong. Further, if you're working on Tor-related
research, please talk to us (https://torproject.org/research)
so we can explain what's available and help interpret your results.

\subsubsection*{Some open questions from the anonymity field.}

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\symbolfootnote[0]{This work is available under the Creative Commons
Attribution (CC-BY) License.}

Here are a few examples of open anonymity and blocking-resistance
problems:

1) How effective is the traffic correlation attack really? Tor's threat
model assumes that an adversary who can see a traffic flow into the Tor
network and the corresponding flow out of the Tor network can correlate
them with high probability and low false positives. Recent results from
Steven Murdoch~\cite{murdoch-pet2007} show confirmation attacks even when both
sides only see a small sample of traffic on each side. But how quick
can the attack actually be in practice, using how little traffic? Are
there effective padding schemes to make correlation less effective?

2) For various diversity metrics (like entropy), how has the diversity
of the Tor network changed over time? How robust is it to change or
attack?{\footnote{https://blog.torproject.org/blog/research-problem-measuring-safety-tor-network}}

3) How can we automatically recognize blocking events---when Tor relays
are censored at a firewall by destination address or by traffic flow
characteristics?

4) Clients who are censored from the public Tor relays can use private
addresses to ``bridge'' into the public Tor network. What strategies
should we use to give out these addresses such that legitimate users
get enough addresses but adversaries can't learn too many?

5) How can we make it hard for censors to recognize Tor traffic flows by
content (e.g.\ distinguishing Tor's handshake from other expected protocols)
and by traffic characteristics (packet size, volume, and timing)? We need
\emph{obfuscation} metrics to let us anticipate which protocols will
blend in better with background traffic or otherwise defeat deep packet
inspection (DPI) algorithms.

\bibliographystyle{plain} \bibliography{crypto2011-tor}
\end{document}

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