Reminder, this showcase is starting in 5 minutes.  See the stream here:
https://www.youtube.com/watch?v=Xle0oOFCNnk

Join us on Freenode at #wikimedia-research
<http://webchat.freenode.net/?channels=wikimedia-research> to ask Andrei
questions.

-Aaron

On Tue, Mar 15, 2016 at 12:53 PM, Dario Taraborelli <
dtarabore...@wikimedia.org> wrote:

> This month, our research showcase
> <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#March_2016> hosts
> Andrei Rizoiu (Australian National University) to talk about his work
> <http://cm.cecs.anu.edu.au/post/wikiprivacy/> on *how private traits of
> Wikipedia editors can be exposed from public data* (such as edit
> histories) using off-the-shelf machine learning techniques. (abstract below)
>
> If you're interested in learning what the combination of machine learning
> and public data mean for privacy and surveillance, come and join us this 
> *Wednesday
> March 16* at *1pm Pacific Time*.
>
> The event will be recorded and publicly streamed
> <https://www.youtube.com/watch?v=Xle0oOFCNnk>. As usual, we will be
> hosting the conversation with the speaker and Q&A on the
> #wikimedia-research channel on IRC.
>
> Looking forward to seeing you there,
>
> Dario
>
>
> Evolution of Privacy Loss in WikipediaThe cumulative effect of collective
> online participation has an important and adverse impact on individual
> privacy. As an online system evolves over time, new digital traces of
> individual behavior may uncover previously hidden statistical links between
> an individual’s past actions and her private traits. To quantify this
> effect, we analyze the evolution of individual privacy loss by studying
> the edit history of Wikipedia over 13 years, including more than 117,523
> different users performing 188,805,088 edits. We trace each Wikipedia’s
> contributor using apparently harmless features, such as the number of edits
> performed on predefined broad categories in a given time period (e.g.
> Mathematics, Culture or Nature). We show that even at this unspecific level
> of behavior description, it is possible to use off-the-shelf machine
> learning algorithms to uncover usually undisclosed personal traits, such as
> gender, religion or education. We provide empirical evidence that the
> prediction accuracy for almost all private traits consistently improves
> over time. Surprisingly, the prediction performance for users who stopped
> editing after a given time still improves. The activities performed by new
> users seem to have contributed more to this effect than additional
> activities from existing (but still active) users. Insights from this work
> should help users, system designers, and policy makers understand and make
> long-term design choices in online content creation systems.
>
>
> *Dario Taraborelli  *Head of Research, Wikimedia Foundation
> wikimediafoundation.org • nitens.org • @readermeter
> <http://twitter.com/readermeter>
>
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>
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