Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase
Authors: Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Patrick Tague, Yuan Tian | Published: 2018-12-07
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Knockoff Nets: Stealing Functionality of Black-Box Models
Authors: Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz | Published: 2018-12-06
The Limitations of Model Uncertainty in Adversarial Settings
Authors: Kathrin Grosse, David Pfaff, Michael Thomas Smith, Michael Backes | Published: 2018-12-06 | Updated: 2019-11-17
Prior Networks for Detection of Adversarial Attacks
Authors: Andrey Malinin, Mark Gales | Published: 2018-12-06
On Configurable Defense against Adversarial Example Attacks
Authors: Bo Luo, Min Li, Yu Li, Qiang Xu | Published: 2018-12-06
When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection
Authors: Xiangyun Tang, Liehuang Zhu, Meng Shen, Xiaojiang Du | Published: 2018-12-06
Differentially Private Data Generative Models
Authors: Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaarfar, Haojin Zhu | Published: 2018-12-06
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge
Authors: Jinyuan Jia, Neil Zhenqiang Gong | Published: 2018-12-05 | Updated: 2018-12-11
Regularized Ensembles and Transferability in Adversarial Learning
Authors: Yifan Chen, Yevgeniy Vorobeychik | Published: 2018-12-05
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples
Authors: Huangyi Ge, Sze Yiu Chau, Bruno Ribeiro, Ninghui Li | Published: 2018-12-05 | Updated: 2020-01-20