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Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakage of sensitive and proprietary training data.
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Yanmin Sun, Andrew K. C. Wong, and Mohamed S. Kamel · 2009
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Elena Zheleva and Lise Getoor · 2009
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Jeremy C Weiss, Sriraam Natarajan, Peggy L Peissig, Catherine A McCarty, and David Page · 2012
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Private traits and attributes are predictable from digital records of human behavior
Michal Kosinski, David Stillwell, and Thore Graepel · 2013
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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Diederik P Kingma and Jimmy Ba · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
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Artificial intelligence in financial markets
Christian Dunis, Peter W Middleton, A Karathanasopolous, and K Theofilatos · 2016
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Neil Zhenqiang Gong and Bin Liu · 2018
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Big data analytics for personalized medicine
Davide Cirillo and Alfonso Valencia · 2019
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Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang · 2019
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Membership inference attacks against machine learning models
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Jinyuan Jia, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong · 2017
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Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes
S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hanaoka · 2017
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Privacy risks of explaining machine learning models
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Model inversion attacks against collaborative inference
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Mlprivacyguard: Defeating confidence information based model inversion attacks on machine learning systems
Tiago A. O. Alves, Felipe M. G. França, and Sandip Kundu · 2019
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The secret revealer: Generative model-inversion attacks against deep neural networks
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Network on network for tabular data classification in real-world applications
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Joint item recommendation and attribute inference: An adaptive graph convolutional network approach
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Defending model inversion and membership inference attacks via prediction purification, 2020
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