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In this work, we study the applications of differential privacy (DP) in the context of graph-structured data.
Zero-knowledge proofs of identity
Uriel Feige, Amos Fiat, and Adi Shamir · 1988
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A graph-grammar approach to represent causal, temporal and other contexts in an oncological patient record
R Müller, O Thews, C Rohrbach, M Sergl, and K Pommerening · 1996
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Universal electronic health record mudr
M Duplaga et al · 2004
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Accurate estimation of the degree distribution of private networks
Michael Hay, Chao Li, Gerome Miklau, and David Jensen · 2009
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A differentially private graph estimator
Darakhshan J Mir and Rebecca N Wright · 2009
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Bigml: A location model with individual waypoint graphs for indoor location-based services
Moritz Kessel, Peter Ruppel, and Florian Gschwandtner · 2010
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Private analysis of graph structure
Vishesh Karwa, Sofya Raskhodnikova, Adam Smith, and Grigory Yaroslavtsev · 2011
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Towards privacy for social networks: A zero-knowledge based definition of privacy
Johannes Gehrke, Edward Lui, and Rafael Pass · 2011
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Personalized social recommendations-accurate or private?
Ashwin Machanavajjhala, Aleksandra Korolova, and Atish Das Sarma · 2011
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Sharing graphs using differentially private graph models
Alessandra Sala, Xiaohan Zhao, Christo Wilson, Haitao Zheng, and Ben Y Zhao · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Brain graphs: graphical models of the human brain connectome
Edward T Bullmore and Danielle S Bassett · 2011
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
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Differentially private graphical degree sequences and synthetic graphs
Vishesh Karwa and Aleksandra B Slavković · 2012
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A differentially private estimator for the stochastic kronecker graph model
Darakhshan Mir and Rebecca N Wright · 2012
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From simple graphs to the connectome: networks in neuroimaging
Olaf Sporns · 2012
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Differentially private data analysis of social networks via restricted sensitivity
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2013
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Recursive mechanism: towards node differential privacy and unrestricted joins
Shixi Chen and Shuigeng Zhou · 2013
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Analyzing graphs with node differential privacy
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2013
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Mining frequent graph patterns with differential privacy
Entong Shen and Ting Yu · 2013
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Differential privacy preserving spectral graph analysis
Yue Wang, Xintao Wu, and Leting Wu · 2013
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Preserving differential privacy in degree-correlation based graph generation
Yue Wang and Xintao Wu · 2013
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Unique in the crowd: The privacy bounds of human mobility
Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel · 2013
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Machine learning with brain graphs: predictive modeling approaches for functional imaging in systems neuroscience
Jonas Richiardi, Sophie Achard, Horst Bunke, and Dimitri Van De Ville · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Correlated network data publication via differential privacy
Rui Chen, Benjamin CM Fung, S Yu Philip, and Bipin C Desai · 2014
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Exponential random graph estimation under differential privacy
Wentian Lu and Gerome Miklau · 2014
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Calibrating data to sensitivity in private data analysis: A platform for differentially-private analysis of weighted datasets
Davide Proserpio, Sharon Goldberg, and Frank McSherry · 2014
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What should we protect? defining differential privacy for social network analysis
Christine Task and Chris Clifton · 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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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Temporal phenotyping from longitudinal electronic health records: A graph based framework
Chuanren Liu, Fei Wang, Jianying Hu, and Hui Xiong · 2015
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Assessing dynamic brain graphs of time-varying connectivity in fmri data: application to healthy controls and patients with schizophrenia
Qingbao Yu, Erik B Erhardt, Jing Sui, Yuhui Du, Hao He, Devon Hjelm, Mustafa S Cetin, Srinivas Rachakonda, Robyn L Miller, Godfrey Pearlson, et al · 2015
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Fundamental Texts On European Private Law
Oliver Radley-Gardner, Hugh Beale, and Reinhard Zimmermann, editors · 2016
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Dependence makes you vulnberable: Differential privacy under dependent tuples
Changchang Liu, Supriyo Chakraborty, and Prateek Mittal · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Differentially private analysis on graphs, 2016
Pennsylvania State University Adam Smith · 2016
Cited alongside, same era.
Publishing graph degree distribution with node differential privacy
Wei-Yen Day, Ninghui Li, and Min Lyu · 2016
Cited alongside, same era.
Publishing attributed social graphs with formal privacy guarantees
Zach Jorgensen, Ting Yu, and Graham Cormode · 2016
Cited alongside, same era.
Lipschitz extensions for node-private graph statistics and the generalized exponential mechanism
Sofya Raskhodnikova and Adam Smith · 2016
Cited alongside, same era.
Using randomized response for differential privacy preserving data collection
Publishing node strength distribution with node differential privacy
Ganghong Liu, Xuebin Ma, and Wuyungerile Li · 2020
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Community preserved social graph publishing with node differential privacy
Sen Zhang, Weiwei Ni, and Nan Fu · 2020
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Differentially-private control-flow node coverage for software usage analysis
Hailong Zhang, Sufian Latif, Raef Bassily, and Atanas Rountev · 2020
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View-gcn: View-based graph convolutional network for 3d shape analysis
Xin Wei, Ruixuan Yu, and Jian Sun · 2020
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Mgnn: a multimodal graph neural network for predicting the survival of cancer patients
Jianliang Gao, Tengfei Lyu, Fan Xiong, Jianxin Wang, Weimao Ke, and Zhao Li · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
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Yue Wang, Xintao Wu, and Donghui Hu · 2016
Cited alongside, same era.
Learning graph-based poi embedding for location-based recommendation
Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang · 2016
Cited alongside, same era.
Shortest paths and distances with differential privacy
Adam Sealfon · 2016
Cited alongside, same era.
Discrete distribution estimation under local privacy
Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
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Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, and Andrew Trask · 2020
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Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy BR Hayter, Richard Vickers, Charles Roberts, Jian Tang, et al · 2021
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Privacy-preserving graph convolutional networks for text classification
Timour Igamberdiev and Ivan Habernal · 2021
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Releasing graph neural networks with differential privacy guarantees
Iyiola E Olatunji, Thorben Funke, and Megha Khosla · 2021
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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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Membership inference attack on graph neural networks
Iyiola E Olatunji, Wolfgang Nejdl, and Megha Khosla · 2021
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Node-level membership inference attacks against graph neural networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang · 2021
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dk-projection: Publishing graph joint degree distribution with node differential privacy
Masooma Iftikhar and Qing Wang · 2021
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Differentially private algorithms for graphs under continual observation
Hendrik Fichtenberger, Monika Henzinger, and Wolfgang Ost · 2021
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Locally differentially private analysis of graph statistics
Jacob Imola, Takao Murakami, and Kamalika Chaudhuri · 2021
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Sensitivity reduction of degree histogram publication under node differential privacy via mean filtering
Sun Lan, Huang Xin, Wu Yingjie, and Guo Yongyi · 2021
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Graph node strength histogram publication method with node differential privacy
Wenfen Liu, Bixia Liu, Qiang Xu, and Hui Lei · 2021
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Efficiently estimating erdos-renyi graphs with node differential privacy
Adam Sealfon and Jonathan Ullman · 2021
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Dpgraph: A benchmark platform for differentially private graph analysis
Siyuan Xia, Beizhen Chang, Karl Knopf, Yihan He, Yuchao Tao, and Xi He · 2021
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Efficient publication of distributed and overlapping graph data under differential privacy
Xu Zheng, Lizong Zhang, Kaiyang Li, and Xi Zeng · 2021
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Network generation with differential privacy
Xu Zheng, Nicholas McCarthy, and Jer Hayes · 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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Braingnn: Interpretable brain graph neural network for fmri analysis
Xiaoxiao Li, Yuan Zhou, Nicha Dvornek, Muhan Zhang, Siyuan Gao, Juntang Zhuang, Dustin Scheinost, Lawrence H Staib, Pamela Ventola, and James S Duncan · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, and Tingjun Hou · 2021
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Graph representation forecasting of patient’s medical conditions: Toward a digital twin
Pietro Barbiero, Ramon Viñas Torné, and Pietro Lió · 2021
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Individual privacy accounting via a renyi filter
Vitaly Feldman and Tijana Zrnic · 2021
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Pre-training graph neural networks for cold-start users and items representation
Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, and Hong Chen · 2021
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Differentially private learning needs better features (or much more data), 2021
Florian Tramèr and Dan Boneh · 2021
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" i need a better description": An investigation into user expectations for differential privacy
Rachel Cummings, Gabriel Kaptchuk, and Elissa M Redmiles · 2021
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Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, and Daniel Rueckert · 2021
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An automatic differentiation system for the age of differential privacy
Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, and Georgios Kaissis · 2021
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Defending medical image diagnostics against privacy attacks using generative methods: Application to retinal diagnostics
William Paul, Yinzhi Cao, Miaomiao Zhang, and Phil Burlina · 2021
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Differentially private graph classification with gnns, 2022
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, and Georgios Kaissis · 2022
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Toward training at imagenet scale with differential privacy, 2022
Alexey Kurakin, Shuang Song, Steve Chien, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
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