暂无分享,去创建一个
Úlfar Erlingsson | Ilya Mironov | Shuang Song | Kunal Talwar | Nicolas Papernot | Ananth Raghunathan | Nicolas Papernot | Kunal Talwar | Ú. Erlingsson | Ilya Mironov | A. Raghunathan | Shuang Song
[1] Aleksey Boyko,et al. Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.
[2] Ilya Mironov,et al. Rényi Differential Privacy , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[3] Mikhail Belkin,et al. Learning privately from multiparty data , 2016, ICML.
[4] Thomas Steinke,et al. Tight Lower Bounds for Differentially Private Selection , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[5] Charu C. Aggarwal,et al. On k-Anonymity and the Curse of Dimensionality , 2005, VLDB.
[6] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[7] Vitaly Shmatikov,et al. Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).
[8] Thomas Steinke,et al. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds , 2016, TCC.
[9] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[10] Guy N. Rothblum,et al. Boosting and Differential Privacy , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[11] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[12] Raef Bassily,et al. Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds , 2014, 1405.7085.
[13] Sofya Raskhodnikova,et al. Smooth sensitivity and sampling in private data analysis , 2007, STOC '07.
[14] Guy N. Rothblum,et al. Concentrated Differential Privacy , 2016, ArXiv.
[15] Guy N. Rothblum,et al. A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[16] Li Zhang,et al. Learning Differentially Private Language Models Without Losing Accuracy , 2017, ArXiv.
[17] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[18] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[19] Peter Harremoës,et al. Rényi Divergence and Kullback-Leibler Divergence , 2012, IEEE Transactions on Information Theory.
[20] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[21] R. Hardwarsing. Stochastic Gradient Descent with Differentially Private Updates , 2018 .
[22] Steve Hanneke,et al. Theory of Disagreement-Based Active Learning , 2014, Found. Trends Mach. Learn..
[23] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[24] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[25] David A. Cohn,et al. Improving generalization with active learning , 1994, Machine Learning.
[26] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[27] Thomas Steinke,et al. Make Up Your Mind: The Price of Online Queries in Differential Privacy , 2016, SODA.
[28] Bhiksha Raj,et al. Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers , 2010, NIPS.
[29] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[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] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Jonathan Ullman,et al. The Price of Selection in Differential Privacy , 2017, COLT.
[34] Tim Roughgarden,et al. Interactive privacy via the median mechanism , 2009, STOC '10.
[35] H. Brendan McMahan,et al. Learning Differentially Private Recurrent Language Models , 2017, ICLR.
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Ian J. Goodfellow,et al. A ug 2 01 7 On the Protection of Private Information in Machine Learning Systems : Two Recent Approaches ( Invited Paper ) , 2018 .
[38] Martín Abadi,et al. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data , 2016, ICLR.
[39] Adam D. Smith,et al. Discovering frequent patterns in sensitive data , 2010, KDD.
[40] Anand D. Sarwate,et al. Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..
[41] Ron Kohavi,et al. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.