White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
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Cordelia Schmid | Matthijs Douze | Hervé Jégou | Yann Ollivier | Alexandre Sablayrolles | Y. Ollivier | C. Schmid | H. Jégou | Alexandre Sablayrolles | Matthijs Douze
[1] Mario Fritz,et al. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models , 2018, NDSS.
[2] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[3] Somesh Jha,et al. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , 2017, 2018 IEEE 31st Computer Security Foundations Symposium (CSF).
[4] Emiliano De Cristofaro,et al. LOGAN: Membership Inference Attacks Against Generative Models , 2017, Proc. Priv. Enhancing Technol..
[5] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[6] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[7] Fabio Roli,et al. Security Evaluation of Support Vector Machines in Adversarial Environments , 2014, ArXiv.
[8] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Úlfar Erlingsson,et al. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.
[10] Raef Bassily,et al. Algorithmic stability for adaptive data analysis , 2015, STOC.
[11] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[12] Kai Chen,et al. Understanding Membership Inferences on Well-Generalized Learning Models , 2018, ArXiv.
[13] A. V. D. Vaart,et al. Asymptotic Statistics: Frontmatter , 1998 .
[14] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Alexander J. Smola,et al. Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo , 2015, ICML.
[16] Emiliano De Cristofaro,et al. LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks , 2017, ArXiv.
[17] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[18] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[19] Armand Joulin,et al. Unsupervised Learning by Predicting Noise , 2017, ICML.
[20] Ling Huang,et al. Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning , 2009, J. Priv. Confidentiality.
[21] S. Kullback,et al. Information Theory and Statistics , 1959 .
[22] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[23] Stephen E. Fienberg,et al. On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms , 2016, PSD.
[24] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2016, J. Priv. Confidentiality.
[25] Giovanni Felici,et al. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers , 2013, Int. J. Secur. Networks.
[26] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[27] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[28] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[29] Úlfar Erlingsson,et al. The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets , 2018, ArXiv.
[30] Michael I. Jordan,et al. Genomic privacy and limits of individual detection in a pool , 2009, Nature Genetics.
[31] Thomas Steinke,et al. Robust Traceability from Trace Amounts , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.