Deep Learning with Differential Privacy

Keywords

Differential privacy, Computer science, Variety (cybernetics), Machine learning, Deep learning

References

• On Random Weights and Unsupervised Feature LearningAndrew Saxe, Pang Wei Koh, Zhenghao Chen — 2011
• Torch7: A Matlab-like Environment for Machine LearningRonan Collobert, Koray Kavukcuoglu, Clément Farabet — 2011
• Learning representations by back-propagating errorsDavid E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams — 1986
• On k-Anonymity and the Curse of DimensionalityCharų C. Aggarwal — 2005
• Efficient Estimation of Word Representations in Vector SpaceTomáš Mikolov, Kai Chen, Greg S. Corrado — 2013
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationKaiming He, Xiangyu Zhang, Shaoqing Ren — 2015
• The Composition Theorem for Differential PrivacyPeter Kairouz, Sewoong Oh, Pramod Viswanath — 2013
• Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error BoundsRaef Bassily, Adam Smith, Abhradeep Thakurta — 2014
• Lua—An Extensible Extension LanguageRoberto Ierusalimschy, Luiz Henrique de Figueiredo, Waldemar Celes Filho — 1996
• Bounds on the sample complexity for private learning and private data releaseAmos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan — 2010
• The cost of privacy: destruction of data-mining utility in anonymized data publishingJustin Brickell, Vitaly Shmatikov — 2008
• Stochastic gradient descent with differentially private updatesShuang Song, Kamalika Chaudhuri, Anand D. Sarwate — 2013
• Private Empirical Risk Minimization: Efficient Algorithms and Tight Error BoundsRaef Bassily, Adam Smith, Abhradeep Thakurta — 2014
• Analyze gauss: optimal bounds for privacy-preserving principal component analysisCynthia Dwork, Kunal Talwar, Abhradeep Thakurta — 2014
• Differentially private recommender systems: building privacy into the netFrank McSherry, Ilya Mironov — 2009
• The Algorithmic Foundations of Differential PrivacyCynthia Dwork, Aaron Roth — 2014
• Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresMatt Fredrikson, Somesh Jha, Thomas Ristenpart — 2015
• Privacy-preserving deep learningReza Shokri, Vitaly Shmatikov — 2015
• A firm foundation for private data analysisCynthia Dwork — 2011
• Privacy integrated queries: an extensible platform for privacy-preserving data analysisFrank McSherry — 2009

Cited by

• Realizing private and practical pharmacological collaboration雅敏 内田 — 2018
• Transitive Transfer LearningBen Tan, Yangqiu Song, Erheng Zhong — 2015
• Differentially Private Stochastic Gradient Descent for in-RDBMS AnalyticsXi Wu, Arun Kumar, Kamalika Chaudhuri — 2016
• Concrete Problems in AI SafetyRicky Aliae — 2016
• Model-based Differentially Private Data Synthesis and Statistical Inference in Multiple Synthetic DatasetsFang Liu — 2016
• Web Information Systems Engineering: WISE 2019 Workshop, Demo, and Tutorial, Hong Kong and Macau, China, January 19–22, 2020, Revised Selected PapersLeong Hou U, Jian Yang, Yi Cai — 2020
• Distributed Optimization for Client-Server Architecture with Negative Gradient WeightsShripad Gade, Nitin H. Vaidya — 2016
• Federated Optimization: Distributed Machine Learning for On-Device IntelligenceJakub Konečný, H. Brendan McMahan, Daniel Ramage — 2016
• Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song — 2016
• Communication-Efficient Learning of Deep Networks from Decentralized DataH. Brendan McMahan, Eider Moore, Daniel Ramage — 2016
• Variational Bayes In Private Settings (VIPS)Mijung Park, James R. Foulds, Kamalika Chaudhuri — 2016
• Differential Privacy in the Wild: A Tutorial on Current Practices & Open ChallengesAshwin Machanavajjhala, Xi He, Michael Hay — 2016
• Differential Private Noise Adding Mechanism: Fundamental Theory and its ApplicationJianping He, Lin Cai — 2016
• Simple Black-Box Adversarial Perturbations for Deep NetworksNina Narodytska, Shiva Prasad Kasiviswanathan — 2016
• Differentially private publication of location entropyHien To, Kien Nguyen, Cyrus Shahabi — 2016
• SoK: Security and Privacy in Machine LearningNicolas Papernot, Patrick McDaniel, Arunesh Sinha — 2018
• Practical Secure Aggregation for Federated Learning on User-Held DataBonawitz, Keith, Vladimir Ivanov, Ben Kreuter — 2016
• Privacy Preservation for Cloud-Based Data Sharing and Data AnalyticsYao Zheng — 2016
• Deep Reinforcement Learning: An OverviewYuxi Li — 2017
• Private Learning on NetworksShripad Gade, Nitin H. Vaidya — 2016

Related papers

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