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As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent.
The political blogosphere and the 2004 us election: divided they blog
Lada A Adamic and Natalie Glance · 2005
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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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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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 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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Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Thomas N Kipf and Max Welling · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Mcne: An end-to-end framework for learning multiple conditional network representations of social network
Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su · 2019
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Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 2019
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Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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Differential privacy for stackelberg games
Ferdinando Fioretto, Lesia Mitridati, and Pascal Van Hentenryck · 2020
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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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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An iterative multi-source mutual knowledge transfer framework for machine reading comprehension
Xin Liu, Kai Liu, Xiang Li, Jinsong Su, Yubin Ge, Bin Wang, and Jiebo Luo · 2020
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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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Stealing links from graph neural networks
Xinlei He, Jin-Yuan Jia, M. Backes, N. Gong, and Y. Zhang · 2021
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