[tor-dev] Anonymous Local Count Statistics Using PCSA - GSoC

samir menon menon.samir at gmail.com
Thu Mar 30 01:45:25 UTC 2017

Hi there!

I'm Samir, a Computer Science student at Stanford University, with a
focus in applied cryptography and computer security. This summer, I
want to work (through GSoC) on computing usage statistics without
keeping IP addresses in memory (see tickets #7532 and #15469) [1] [2].

Currently, we keep sets of IP's (or hashed IP's) in memory so that we
can compute the number of unique client connections. This has been
pointed out as a pretty serious concern, because the IP's themselves
are sensitive info that we don't want an attacker to acquire, but the
statistics are relatively valuable.

As Nick first pointed out in #15469, we can use proven techniques to
compute these statistics without actually explicitly storing any IP's
(or IP hashes) in memory. The technique I want to use, "Probabilistic
Counting with Stochastic Averaging", or PCSA, is relatively
well-studied, and can provide good estimates (<5% error) of the number
of unique elements in a time series.

The basic idea is to count the number of 0's before the least
significant 1 in every (Jenkins hashed) IP, and then recognize that
the more unique IP's we encounter, the more likely it is that we see a
hashed IP with a large number of 0's before the least significant 1.
(Shoutout to Jaskaran and [3] for helping me understand this). A more
detailed explanation and more resources for understanding PCSA are in
the proposal.

Here is my draft proposal (also attached, but links don't work):

I'd love to hear feedback on it - what's feasible, what's most useful,
and what I should focus on, etc. You can also chat with me about it on
IRC at `samir2`!

~Samir Menon
menon.samir at gmail.com
Stanford University, B.S. Computer Science, 2019

[1] https://trac.torproject.org/projects/tor/ticket/7532
[2] https://trac.torproject.org/projects/tor/ticket/15469
[3] https://www.cs.princeton.edu/~rs/talks/AC11-Cardinality.pdf
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