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Increasing use of 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 leakages of sensitive and proprietary training data.
Comparison of the predicted and observed secondary structure of t4 phage lysozyme
B.W. Matthews · 1975
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Neural networks in finance and investing: Using artificial intelligence to improve real world performance
Robert R Trippi and Efraim Turban · 1992
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Amazon.com recommendations: item-to-item collaborative filtering
G. Linden, B. Smith, and J. York · 2003
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Estimation of the warfarin dose with clinical and pharmacogenetic data
International Warfarin Pharmacogenetics Consortium · 2009
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A survey of collaborative filtering techniques
Xiaoyuan Su and Taghi M Khoshgoftaar · 2009
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Classification of imbalanced data: a review
Yanmin Sun, Andrew K. C. Wong, and Mohamed S. Kamel · 2009
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To join or not to join: The illusion of privacy in social networks with mixed public and private user profiles
Elena Zheleva and Lise Getoor · 2009
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Machine learning for personalized medicine: Predicting primary myocardial infarction from electronic health records
Jeremy C Weiss, Sriraam Natarajan, Peggy L Peissig, Catherine A McCarty, and David Page · 2012
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You are what you like! information leakage through users’ interests
Abdelberi Chaabane, Gergely Acs, and Mohamed Ali Kaafar · 2012
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Blurme: Inferring and obfuscating user gender based on ratings
Udi Weinsberg, Smriti Bhagat, Stratis Ioannidis, and Nina Taft · 2012
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The power of prediction with social media
Daniel Gayo-Avello, Panagiotis Takis Metaxas, Eni Mustafaraj, Markus Strohmaier, Harald Schoen, and Peter Gloor · 2013
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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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Joint link prediction and attribute inference using a social-attribute network
Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine (Runting) Shi, and Dawn Song · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Artificial intelligence in financial markets
Christian Dunis, Peter W Middleton, A Karathanasopolous, and K Theofilatos · 2016
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Attribute inference attacks in online social networks
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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Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, and Michael M Hoffman · 2019
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Gamin: An adversarial approach to black-box model inversion
Ulrich Aïvodji, Sébastien Gambs, and Timon Ther · 2019
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Neural network inversion in adversarial setting via background knowledge alignment
Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang · 2019
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Computational personality recognition in social media
Golnoosh Farnadi, Geetha Sitaraman, Shanu Sushmita, Fabio Celli, Michal Kosinski, David Stillwell, Sergio Davalos, Marie-Francine Moens, and Martine De Cock · 2016
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Fusing social media cues: personality prediction from twitter and instagram
Marcin Skowron, Marko Tkalčič, Bruce Ferwerda, and Markus Schedl · 2016
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A methodology for formalizing model-inversion attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton · 2016
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You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors
Neil Zhenqiang Gong and Bin Liu · 2016
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Attriinfer: Inferring user attributes in online social networks using markov random fields
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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Law as computation in the era of artificial legal intelligence: Speaking law to the power of statistics
Mireille Hildebrandt · 2018
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Reza Shokri, Martin Strobel, and Yair Zick · 2019
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Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B. Lee · 2019
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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
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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Joint item recommendation and attribute inference: An adaptive graph convolutional network approach
Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang · 2020
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Defending model inversion and membership inference attacks via prediction purification, 2020
Ziqi Yang, Bin Shao, Bohan Xuan, Ee-Chien Chang, and Fan Zhang · 2020
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