https://nakedsecurity.sophos.com/2017/02/24/twitter-users-do
-you-know-whos-spying-on-your-web-surfing-habits/


Twitter users, do you know who’s spying on your web-surfing habits?

24 Feb 2017

by Danny Bradbury <https://nakedsecurity.sophos.com/author/dannybradbury/>

If you weren’t already worried about the privacy dangers of online ad
tracking, now would be a good time to start. Researchers have found a way
to de-anonymise web surfing records, putting a recent US privacy ruling in
jeopardy.

Online ad networks track your browsing history across multiple sites so
that they can serve you more effective advertising. Search for something on
an ecommerce site that participates in one of these networks, and you’ll be
shown related ads on another site that also participates.

This often creeps people out, but the counterargument has always been that
the data is anonymous. Instead of linking your real name to your web
surfing records, these trackers use a unique customer ID.

In this way, they may know that the same person who searched for
Christmas getaways on one website is now reading an article about marmosets
on another website, but because they don’t know who that person is, they
can responsibly litter the marmoset article with holiday advertisements,
without anyone on the internet knowing that you’re leaving your house
unattended on December 25.

That’s all well and good, but what if you could deduce a person’s
identity by matching their anonymous web surfing with their social media
timeline? What if, instead of a customer ID, you could replace it with
their Twitter handle?
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Academics from Stanford and Princeton have done just that. Their research
<http://randomwalker.info/publications/browsing-history-deanonymization.pdf>
relies on the idea that people are more likely to follow links showing up
on their social media feed, and in particular the links from people they
follow on Twitter that show up in their feed. They reasoned that because
the set of links in a Twitter feed is often unique, you can match it
against links in an anonymous surfing history.

The group collected anonymous web browsing histories from almost 400
volunteers, and mined them for links that came from Twitter (marked with
the domain name t.co, which Twitter uses to shorten URLs) visited in the
last 30 days. It attempted to de-anonymize histories with at least five
such links by comparing them against 300m Twitter feeds.

The researchers found that they could identify more than 70% of volunteers
on average. The more links in someone’s history that originated from
Twitter, the more accurate the identification. The team correctly
identified 86% of participants in the experiment with between 50 and 75
URLs. So if you follow a lot of links from Twitter, you’re more likely to
be identified.

This isn’t just a theoretical exercise. The team built a system to
de-anonymise web browsing histories in under a minute using the concept,
proving that it’s workable in practice.

The team at Princeton has a record of exposing flaws in anonymous
datasets. Arvind Narayanan, one of the researchers, runs a blog called 33
Bits of Entropy <https://33bits.org/>, named for the fact that there are
about 6.6bn people in the world, meaning that you only need 33 bits of
information to determine their identity. He has moved on a bit from his
de-anonymising research, but in the past he has embarrassed Netflix
<https://www.wired.com/2012/06/wmw-arvind-narayanan/> by using its research
dataset to work out who was watching what movies.

Here’s another tidbit from the research: it points out that the same
principles apply to any set of items selected anonymously by someone with
an identifiable historical record of selections. For example, anonymous
papers might cite other work and could be compared with a broader spectrum
of academic papers to see if similarities show up.

We wonder if it’s possible to run it against the eight references in the
original bitcoin paper <https://bitcoin.org/bitcoin.pdf>, created by the
mysterious Satoshi Nakamoto
<https://nakedsecurity.sophos.com/2014/09/10/bitcoin-inventors-identity-allegedly-under-threat-after-claimed-satoshi-nakamoto-email-hijack/>,
to help track him down, assuming that he had published academic work
before? Not necessarily, says Jessica Su, one of the researchers:

I wouldn’t be able to tell you without having access to a dataset that
included that paper. However, I am currently trying to deanonymize physics
papers, and a modification of our method gives us 28% accuracy.  If we
limit to papers with exactly eight intra-database citations, we get 36%
accuracy.  These are very preliminary results.

A one in three chance of finding Satoshi, maybe? Sounds better
<http://www.newsweek.com/newsweeks-statement-bitcoin-story-231242> than
most other options
<https://www.theguardian.com/technology/2015/dec/10/bitcoin-creator-satoshi-nakamoto-denies-being-craig-wright-maybe>
we’ve had.

Who is likely to use social media history in combination with ad tracking?
The trackers themselves could. The team looked at four such trackers:
Google, Facebook, ComScore and AppNexus – and found that they all had
enough information to de-anonymize their users.

Some of these trackers are already de-anonymizing users by default. Google,
which already tracks users across almost 80% of websites, changed its
privacy policy around anonymisation
<https://www.propublica.org/article/google-has-quietly-dropped-ban-on-personally-identifiable-web-tracking>
late last year to match links with Google accounts. Facebook, meanwhile,
owns the social network from which its users follow links.

Who else might use this information? The NSA, for one. It already tracks
Google ads
<https://www.cnet.com/news/nsa-tracks-google-ads-to-find-tor-users/> to
find Tor users. The research points out that well-resourced adversaries
could eavesdrop on network traffic to work out which domains a particular
device is visiting (although thankfully HTTPS makes that more difficult).

Other potential users could include potential employers, anyone granting
credit, or insurance companies who might love to know about your recent
search for cancer symptoms or risky pursuits. Anyone who could benefit from
knowing what you’re searching for would find this attack useful.

The good news is that commercial parties like these could only match your
anonymous browsing history against your public social media profile if they
had that data. The bad news is that it has long been for sale
<http://www.huffingtonpost.com/2013/07/08/att-selling-data_n_3561263.html>.

It’s particularly galling for privacy advocates, because the selling of
customer data was about to get a lot harder. The FCC in the US issued an
order <https://apps.fcc.gov/edocs_public/attachmatch/FCC-16-148A1_Rcd.pdf>
restricting ISPs from collecting customers’ sensitive data unless they
specifically opted in. Under the order, service providers must get customer
permission to sell sensitive personal data, defined as “reasonably
linkable” to an individual.

Anonymised data may be seen as not reasonably linkable, meaning that it can
be collected and used. But clearly, with a bit of automated detective work,
it’s pretty easy to make that link.

How can you stop this from happening? Tracker-blockers such as Ghostery
<https://www.ghostery.com/>, uBlock Origin
<https://en.wikipedia.org/wiki/UBlock_Origin> or Privacy Badger
<https://www.eff.org/privacybadger>can help, the researchers say, while not
revealing your real-world identity on social media profiles is a useful
albeit cumbersome form of protection. Given the recent actions of US border
guards
<http://www.cnbc.com/2017/01/30/trumps-executive-order-spurs-facebook-and-twitter-checks-at-the-border.html>,
the latter might be a good idea anyway.






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