7 citations · 14 across the 5 of their papers we have counts for
6 papers
Robust Single-message Shuffle Differential Privacy Protocol for Accurate Distribution Estimation
Xiaoguang Li, Hanyi Wang, Yaowei Huang +6
Shuffler-based differential privacy (shuffle-DP) is a privacy paradigm providing high utility by involving a shuffler to permute noisy report from users. Existing shuffle-DP protoc…
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts
Xiongtao Sun, Gan Liu, Zhipeng He +2
Prompt serves as a crucial link in interacting with large language models (LLMs), widely impacting the accuracy and interpretability of model outputs. However, acquiring accurate a…
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
Haonan Yan, Wenjing Zhang, Qian Chen +4
Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks,…
Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation
Xiaoguang Li, Ninghui Li, Wenhai Sun +2
Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poison…
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack
Haonan Yan, Xiaoguang Li, Hui Li +3
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through…
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Xiaoguang Li, Hui Li, Haonan Yan +3
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through…