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Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public.
Collective classification in network data
Prithviraj Sen, Galileo Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
Cited alongside, same era.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
Cited alongside, same era.
Large-scale differentially private bert
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi · 2021
Cited alongside, same era.
Graphmi: Extracting private graph data from graph neural networks
Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, and Enhong Chen · 2021
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Node-level differentially private graph neural networks
Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, and Prateek Jain · 2021
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Locally private graph neural networks
Sina Sajadmanesh and Daniel Gatica-Perez · 2021
Later among the works it cites.
Node-level differentially private graph neural networks
Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, and Prateek Jain · 2022
Later among the works it cites.
Gap: Differentially private graph neural networks with aggregation perturbation
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez · 2022
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Linkteller: Recovering private edges from graph neural networks via influence analysis
Fan Wu, Yunhui Long, Ce Zhang, and Bo Li · 2021
Cited alongside, same era.
Membership inference attack on graph neural networks
Iyiola E Olatunji, Wolfgang Nejdl, and Megha Khosla · 2021
Cited alongside, same era.
Later among the works it cites.
Privacy-Preserving Graph Convolutional Networks for Text Classification
Timour Igamberdiev and Ivan Habernal · 2022
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Sok: Differential privacy on graph-structured data
Tamara T Mueller, Dmitrii Usynin, Johannes C Paetzold, Daniel Rueckert, and Georgios Kaissis · 2022
Later among the works it cites.