Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions
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[1] Suman Jana,et al. On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning , 2018, ArXiv.
[2] Kamalika Chaudhuri,et al. Privacy-preserving logistic regression , 2008, NIPS.
[3] Robert Laganière,et al. Membership Inference Attack against Differentially Private Deep Learning Model , 2018, Trans. Data Priv..
[4] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[5] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[6] Dejing Dou,et al. Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction , 2016, AAAI.
[7] Giovanni Felici,et al. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers , 2013, Int. J. Secur. Networks.
[8] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[9] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[10] Giuseppe Ateniese,et al. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.
[11] Emiliano De Cristofaro,et al. Differentially Private Mixture of Generative Neural Networks , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[12] Vitaly Shmatikov,et al. Privacy-preserving deep learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[13] Úlfar Erlingsson,et al. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response , 2014, CCS.
[14] Anand D. Sarwate,et al. Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..
[15] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[16] Úlfar Erlingsson,et al. Scalable Private Learning with PATE , 2018, ICLR.
[17] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[18] Moni Naor,et al. Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.
[19] Somesh Jha,et al. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing , 2014, USENIX Security Symposium.
[20] Dejing Dou,et al. Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[21] Ilya Mironov,et al. Rényi Differential Privacy , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[22] Ninghui Li,et al. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[23] Fumiyuki Adachi,et al. Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding , 2019, IEEE Transactions on Vehicular Technology.
[24] Raymond Chi-Wing Wong,et al. (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.
[25] Jerome P. Reiter,et al. Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data , 2014, J. Priv. Confidentiality.
[26] Yin Yang,et al. Functional Mechanism: Regression Analysis under Differential Privacy , 2012, Proc. VLDB Endow..
[27] Reza Shokri,et al. Synthesizing Plausible Privacy-Preserving Location Traces , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[28] T. J. Rivlin. Chebyshev polynomials : from approximation theory to algebra and number theory , 1990 .
[29] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[30] Carl A. Gunter,et al. Plausible Deniability for Privacy-Preserving Data Synthesis , 2017, Proc. VLDB Endow..
[31] Kunal Talwar,et al. Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).
[32] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[33] Guan Gui,et al. Deep Learning for an Effective Nonorthogonal Multiple Access Scheme , 2018, IEEE Transactions on Vehicular Technology.
[34] Steven Skiena,et al. DeepWalk: online learning of social representations , 2014, KDD.
[35] Guan Gui,et al. Improved Cross-Label Suppression Dictionary Learning for Face Recognition , 2018, IEEE Access.
[36] Úlfar Erlingsson,et al. Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries , 2015, Proc. Priv. Enhancing Technol..
[37] Elisa Bertino,et al. Differentially Private K-Means Clustering and a Hybrid Approach to Private Optimization , 2017, ACM Trans. Priv. Secur..
[38] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[39] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[40] Martín Abadi,et al. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data , 2016, ICLR.
[41] Toniann Pitassi,et al. The reusable holdout: Preserving validity in adaptive data analysis , 2015, Science.
[42] Guy N. Rothblum,et al. Boosting and Differential Privacy , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[43] Raef Bassily,et al. Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds , 2014, 1405.7085.
[44] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[45] Yin Yang,et al. Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy , 2016, ArXiv.
[46] Yue Yin,et al. Deep Learning-Based Unmanned Surveillance Systems for Observing Water Levels , 2018, IEEE Access.
[47] Wenyi Huang,et al. MtNet: A Multi-Task Neural Network for Dynamic Malware Classification , 2016, DIMVA.
[48] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[49] Giuseppe Dattoli,et al. Mathematical Methods for Physicists , 2019 .
[50] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[51] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[52] Ashwin Machanavajjhala,et al. l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.
[53] Pierre Baldi,et al. Deep autoencoder neural networks for gene ontology annotation predictions , 2014, BCB.
[54] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[55] Martín Abadi,et al. On the Protection of Private Information in Machine Learning Systems: Two Recent Approches , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[56] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[57] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[58] Peter L. Bartlett,et al. Localized Rademacher Complexities , 2002, COLT.
[59] Shouling Ji,et al. Differentially Private Releasing via Deep Generative Model , 2018, ArXiv.
[60] Jun Tang,et al. Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12 , 2017, ArXiv.
[61] Casey S. Greene,et al. Privacy-preserving generative deep neural networks support clinical data sharing , 2017 .
[62] Thomas Steinke,et al. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds , 2016, TCC.
[63] Boi Faltings,et al. Generating Differentially Private Datasets Using GANs , 2018, ArXiv.
[64] Gyöngyi Bujdosó,et al. Comparative study of Google Android, Apple iOS and Microsoft Windows Phone mobile operating systems , 2017, 2017 14th International Conference on Engineering of Modern Electric Systems (EMES).