Evolution of privacy loss in wikipedia

Rizoiu, MA; Xie, L; Caetano, T; Cebrian, M

Evolution of privacy loss in wikipedia

Rizoiu, MA

Xie, L Caetano, T Cebrian, M

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Publication Type:: Conference Proceeding
Citation:: WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining, 2016, pp. 215 - 224
Issue Date:: 2016-02-08

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Field	Value	Language
dc.contributor.author	Rizoiu, MA https://orcid.org/0000-0003-0381-669X	en_US
dc.contributor.author	Xie, L	en_US
dc.contributor.author	Caetano, T	en_US
dc.contributor.author	Cebrian, M	en_US
dc.date.issued	2016-02-08	en_US
dc.identifier.citation	WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining, 2016, pp. 215 - 224	en_US
dc.identifier.isbn	9781450337168	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135284
dc.description.abstract	© 2015 Copyright held by the owner/author(s). The 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.	en_US
dc.relation.ispartof	WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining	en_US
dc.relation.isbasedon	10.1145/2835776.2835798	en_US
dc.title	Evolution of privacy loss in wikipedia	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 Copyright held by the owner/author(s). The 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.

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http://hdl.handle.net/10453/135284