On Wed, Mar 16, 2016 at 7:53 PM, SarahSV <sarahsv.wiki(a)gmail.com> wrote:
Dario and Aaron, thanks for letting us know about
this. Is the research
available in writing for people who don't want to sit through the video?
Sarah
On Wed, Mar 16, 2016 at 12:55 PM, Aaron Halfaker <ahalfaker(a)wikimedia.org>
wrote:
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 <
dtaraborelli(a)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>
_______________________________________________
Wiki-research-l mailing list
Wiki-research-l(a)lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
_______________________________________________
Wikimedia-l mailing list, guidelines at:
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines
New messages to: Wikimedia-l(a)lists.wikimedia.org
Unsubscribe:
https://lists.wikimedia.org/mailman/listinfo/wikimedia-l,
<mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe>
_______________________________________________
Wikimedia-l mailing list, guidelines at:
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines
New messages to: Wikimedia-l(a)lists.wikimedia.org
Unsubscribe:
https://lists.wikimedia.org/mailman/listinfo/wikimedia-l,
<mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe>