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> > > _______________________________________________ > Wiki-research-l mailing list > Wiki-research-l@lists.wikimedia.org > https://lists.wikimedia.org/mailman/listinfo/wiki-research-l > >
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