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Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes).
Randomized response: a survey technique for eliminating evasive answer bias
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Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Jahna Otterbacher · 2010
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Liran Lerman, Gianluca Bontempi, and Olivier Markowitch · 2011
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Abdelberi Chaabane, Gergely Acs, and Mohamed Ali Kaafar · 2012
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Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, and Dawn Song · 2012
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Michal Kosinski, David Stillwell, and Thore Graepel · 2013
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Raymond Heatherly, Murat Kantarcioglu, and Bhavani Thuraisingham · 2013
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Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim ŚrndićPavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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Jonathon Shlens Ian J. Goodfellow and Christian Szegedy · 2014
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Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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Attribute inference attacks in online social networks
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https://goo.gl/PqRjjX, May 2018
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