Re: [tor-dev] Client simulation

[Karsten Loesing <karsten@xxxxxxxxxxxxxx>]

On 6/7/13 8:04 PM, Norman Danner wrote:
> On 6/7/13 2:37 AM, Karsten Loesing wrote:
>> (Sorry for cross-posting, but I think this is a topic for tor-dev@, not
>> tor-talk@.  If you agree, please reply on tor-dev@ only.  tor-talk@
>> people can follow the thread here:
> 
> OK, following up on tor-dev.  I thought tor-dev might be the more
> appropriate list, but the description for tor-talk is "all discussion
> about theory, design, and development of Onion Routing," whereas that
> for tor-dev is "discussion regarding Tor development."  Maybe since I
> think more about theory than code, the former description seemed more
> applicable...

I haven't looked at list descriptions for a while, but tor-dev@ has
similar discussions like this one, whereas tor-talk@ doesn't.  Consider
this decision for tor-dev@ as the result of human-learning techniques,
rather than an evaluation of list descriptions. ;)

>> On 6/6/13 7:32 PM, Norman Danner wrote:
>>> I have two questions regarding a possible research project.
>>>
>>> First, the research question:  can one use machine-learning techniques
>>> to construct a model of Tor client behavior?  Or in a more general form:
>>>   can one use <fill-in-the-blank> to construct a model of Tor client
>>> behavior?  A student of mine did some work on this over the last year,
>>> and the results are encouraging, though not strong enough to do anything
>>> with yet.
>>>
>>> Second, the meta-question:  is it worthwhile to answer the first
>>> question?
>> [snip]
>> Hi Norman,
>>
>> yes, it's worthwhile to answer this question!  I can imagine how at
>> least Shadow and the Tor path generator would benefit from better client
>> models.  User number estimates on the metrics website might benefit from
>> them, too.
>>
>> I found two tickets where we asked similar questions before, and maybe
>> there are more tickets like these:
>>
>> https://trac.torproject.org/projects/tor/ticket/2963
>>
>> https://trac.torproject.org/projects/tor/ticket/6295
>>
>> Some very early thoughts:
>>
>> - How do we make sure that we ask a representative set of people to
>> instrument their clients and export data on their usage behavior?  If we
>> only ask people who read their favorite news site twice per day, our
>> client model will be just that, but not representative for all Tor
>> users.  (Still, we would know more than we know now.)
>>
>> - Can we somehow aggregate usage information enough to make it safe for
>> people to send actual usage reports to us?  I could imagine having a
>> torrc flag that is disabled by default and that, when enabled, writes
>> sanitized usage information to disk.  For this we need a very good idea
>> what we're planning to do with the data, and we'll need to specify the
>> aggregation approach in a tech report and get it reviewed by the
>> community.
>>
>> Are your student's results available somewhere?
> 
> The written portion of my student Julian Applebaum's Senior thesis is
> available at
> 
>     http://wesscholar.wesleyan.edu/etd_hon_theses/1042/
> 
> Our focus in this project (which I left intentionally vague in my
> posting) was to try to model clients at a very high level. Specifically,
> we wanted to see if we could model something like the timing patterns of
> the Tor cells that clients send to the network.  An intended application
> (not yet completely thought through...) would be to use such information
> to get a more accurate sense of how well timing attacks work, by
> deploying them (in simulation) against presumably realistic clients. 
> Our strategy was roughly:
> 
> * Instrument our guard node to record cell arrival times from clients
> pseudononymously (i.e., we know when two different cells belong to the
> same circuit, but we only record circuits as A, B, etc.).
> * Record such data for a short period of time.
> * Represent each circuit as a time series.
> * Cluster the collection of time series using Markov model clustering
> techniques.
> 
> The intent is that each cluster (represented by a single hidden Markov
> model) represents a "type" of client, even though we don't know for sure
> what that client type does.  We can make some guesses about some:  the
> "type" of steady high-volume cell counts is probably a bulk downloader;
> the "type" of steady zero cell counts is probably an unused circuit;
> etc.  But in some sense, I'm thinking that what counts is the behavior
> of the client, not the reason for that behavior.  We don't have to
> instrument clients for this.  Of course, then one has to ask whether
> this kind of modeling is in fact useful.  It is somewhat different than
> what you are envisioning, I think.
> 
> There are about a billion variations (at last count) on this theme.  We
> chose one particular one as a test case to play with the methodology.  I
> think the methodology is mostly OK, though I'm not completely satisfied
> with the results of the particular variation Julian worked on.  So now
> I'm trying to figure out whether to push this forward and in particular
> what directions and end goals would be useful.

Interesting stuff!  You're indeed taking a different approach than I
were envisioning by gathering data on a single guard rather than on a
set of volunteering clients.  Both approaches have their pros and cons,
but I think your approach leads to some interesting results and can be
done in a privacy-preserving fashion.

Two thoughts:

- I could imagine that your results are quite valuable for modeling
better Shadow/ExperimenTor clients or for deriving better client models
for Tor path simulators.  Maybe Julian's thesis already has some good
data for that, or maybe we'll have to repeat the experiment in a
slightly different setting.  I'm cc'ing Rob (the Shadow author) and
Aaron (working on a path simulator) to make sure they saw this thread.
I can help by reviewing code changes to Tor to make sure data is
gathered in a privacy-preserving way, and I'd appreciate if those code
changes would be made public together with analysis results.

- It might be interesting to observe how Tor usage changes over time.
Maybe the research experiment leads to a set of classifiers telling us
when a circuit is most likely used for bulk downloads, used for web
browsing, used for IRC, unused, or whatever.  We could then extend
circuit statistics to have all relays report aggregate data of how
circuits can be classified.  Requires a proposal and code, but I could
help with those.

Fun stuff!

Best,
Karsten

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