Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
Authors: Rui Wen, Michael Backes, Yang Zhang | Published: 2024-09-05
Privacy Protection Method
Defending against Model Inversion Attacks via Random Erasing
Authors: Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai, Hans Vandierendonck, Ngai-man Cheung | Published: 2024-09-02
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Authors: Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley Malin, Ye Wang | Published: 2024-08-29
LLM-PBE: Assessing Data Privacy in Large Language Models
Authors: Qinbin Li, Junyuan Hong, Chulin Xie, Jeffrey Tan, Rachel Xin, Junyi Hou, Xavier Yin, Zhun Wang, Dan Hendrycks, Zhangyang Wang, Bo Li, Bingsheng He, Dawn Song | Published: 2024-08-23 | Updated: 2024-09-06
Private Counting of Distinct Elements in the Turnstile Model and Extensions
Authors: Monika Henzinger, A. R. Sricharan, Teresa Anna Steiner | Published: 2024-08-21
Tracing Privacy Leakage of Language Models to Training Data via Adjusted Influence Functions
Authors: Jinxin Liu, Zao Yang | Published: 2024-08-20 | Updated: 2024-09-05
Privacy Technologies for Financial Intelligence
Authors: Yang Li, Thilina Ranbaduge, Kee Siong Ng | Published: 2024-08-19
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Authors: Huitong Jin, Yipeng Zhou, Laizhong Cui, Quan Z. Sheng | Published: 2024-08-18
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts
Authors: Xiongtao Sun, Gan Liu, Zhipeng He, Hui Li, Xiaoguang Li | Published: 2024-08-16
Privacy in Federated Learning
Authors: Jaydip Sen, Hetvi Waghela, Sneha Rakshit | Published: 2024-08-12