Efficient Private Machine Learning by Differentiable Random Transformations
Authors: Fei Zheng | Published: 2020-08-18
Membership Inference
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection
Authors: Luca Demetrio, Scott E. Coull, Battista Biggio, Giovanni Lagorio, Alessandro Armando, Fabio Roli | Published: 2020-08-17 | Updated: 2021-03-22
Membership Leakage in Label-Only Exposures
Authors: Zheng Li, Yang Zhang | Published: 2020-07-30 | Updated: 2021-09-17
Label-Only Membership Inference Attacks
Authors: Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot | Published: 2020-07-28 | Updated: 2021-12-05
How Does Data Augmentation Affect Privacy in Machine Learning?
Authors: Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu | Published: 2020-07-21 | Updated: 2021-02-26
Sharing Models or Coresets: A Study based on Membership Inference Attack
Authors: Hanlin Lu, Changchang Liu, Ting He, Shiqiang Wang, Kevin S. Chan | Published: 2020-07-06
Robust and Accurate Authorship Attribution via Program Normalization
Authors: Yizhen Wang, Mohannad Alhanahnah, Ke Wang, Mihai Christodorescu, Somesh Jha | Published: 2020-07-01 | Updated: 2022-02-25
Reducing Risk of Model Inversion Using Privacy-Guided Training
Authors: Abigail Goldsteen, Gilad Ezov, Ariel Farkash | Published: 2020-06-29
RayS: A Ray Searching Method for Hard-label Adversarial Attack
Authors: Jinghui Chen, Quanquan Gu | Published: 2020-06-23 | Updated: 2020-09-05
On Adversarial Bias and the Robustness of Fair Machine Learning
Authors: Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda, Ehsan Kazemi, Reza Shokri | Published: 2020-06-15