Addressing Privacy Threats from Machine Learning
Authors: Mary Anne Smart | Published: 2021-10-25
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
Authors: Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun, Ngai-man Cheung, Yuval Elovici, Alexander Binder | Published: 2021-10-24
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Authors: Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi | Published: 2021-10-22 | Updated: 2022-10-21
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy
Authors: Xiaolan Gu, Ming Li, Li Xiong | Published: 2021-10-22
CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering
Authors: Amir Hosein Afandizadeh Zargari, Marzieh AshrafiAmiri, Minjun Seo, Sai Manoj Pudukotai Dinakarrao, Mohammed E. Fouda, Fadi Kurdahi | Published: 2021-10-21
Privacy in Open Search: A Review of Challenges and Solutions
Authors: Samuel Sousa, Christian Guetl, Roman Kern | Published: 2021-10-20 | Updated: 2022-04-04
Color Teams for Machine Learning Development
Authors: Josh Kalin, David Noever, Matthew Ciolino | Published: 2021-10-20
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems
Authors: Jindi Zhang, Yifan Zhang, Kejie Lu, Jianping Wang, Kui Wu, Xiaohua Jia, Bin Liu | Published: 2021-10-20
Multi-concept adversarial attacks
Authors: Vibha Belavadi, Yan Zhou, Murat Kantarcioglu, Bhavani M. Thuraisingham | Published: 2021-10-19
A ground-truth dataset of real security patches
Authors: Sofia Reis, Rui Abreu | Published: 2021-10-18