ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Authors: Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg | Published: 2024-05-02
Reconstruction Durability
Visual Privacy Auditing with Diffusion Models
Authors: Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller | Published: 2024-03-12
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Authors: Noel Loo, Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus | Published: 2023-02-02 | Updated: 2023-11-09
Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing
Authors: Mohammad Malekzadeh, Deniz Gunduz | Published: 2022-12-08 | Updated: 2024-10-01
Reconstructing Training Data from Model Gradient, Provably
Authors: Zihan Wang, Jason D. Lee, Qi Lei | Published: 2022-12-07 | Updated: 2023-06-10
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
Authors: Pengyu Qiu, Xuhong Zhang, Shouling Ji, Chong Fu, Xing Yang, Ting Wang | Published: 2022-12-01 | Updated: 2024-01-22
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Authors: Kai Yue, Richeng Jin, Chau-Wai Wong, Dror Baron, Huaiyu Dai | Published: 2022-06-08 | Updated: 2022-10-14
Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution
Authors: Yiftach Savransky, Roni Mateless, Gilad Katz | Published: 2021-11-04
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Authors: Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Caiwen Ding, Sanguthevar Rajasekaran | Published: 2020-09-14