Security and Privacy Issues of Federated Learning
Authors: Jahid Hasan | Published: 2023-07-22
Membership Inference
A LLM Assisted Exploitation of AI-Guardian
Authors: Nicholas Carlini | Published: 2023-07-20
Eliminating Label Leakage in Tree-Based Vertical Federated Learning
Authors: Hideaki Takahashi, Jingjing Liu, Yang Liu | Published: 2023-07-19 | Updated: 2023-10-22
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
Authors: Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He | Published: 2023-07-10
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction
Authors: Zitao Chen, Karthik Pattabiraman | Published: 2023-07-04
You Don’t Need Robust Machine Learning to Manage Adversarial Attack Risks
Authors: Edward Raff, Michel Benaroch, Andrew L. Farris | Published: 2023-06-16
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Authors: Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries, Marian Dietz, Faezeh Ebrahimianghazani, Bailey Kacsmar, Xinda Li, Nils Lukas, Rasoul Akhavan Mahdavi, Simon Oya, Ehsan Amjadian, Florian Kerschbaum | Published: 2023-06-14 | Updated: 2024-04-16
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
Authors: Ana-Maria Cretu, Daniel Jones, Yves-Alexandre de Montjoye, Shruti Tople | Published: 2023-06-08 | Updated: 2024-03-12
Machine Unlearning: A Survey
Authors: Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu | Published: 2023-06-06
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Authors: Hubert Baniecki, Przemyslaw Biecek | Published: 2023-06-06 | Updated: 2024-02-13