36 citations · 40 across the 48 of their papers we have counts for
24 papers · 1 filter
Adversarial Signed Graph Learning with Differential Privacy
Haobin Ke, Sen Zhang, Qingqing Ye +2
Signed graphs with positive and negative edges can model complex relationships in social networks. Leveraging on balance theory that deduces edge signs from multi-hop node pairs, s…
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
Yaxin Xiao, Qingqing Ye, Zi Liang +4
Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through…
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Tairan Huang, Yulin Jin, Junxu Liu +2
Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existi…
Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data
Zi Liang, Qingqing Ye, Xuan Liu +3
Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large language models (LLMs) during trainin…
Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts
Li Bai, Qingqing Ye, Xinwei Zhang +4
Machine learning models are often vulnerable to inference attacks that expose sensitive information from their training data. Shadow model technique is commonly employed in such at…
Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Zi Liang, Zhiyao Wu, Haoyang Shang +5
Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties an…
MER-Inspector: Assessing model extraction risks from an attack-agnostic perspective
Xinwei Zhang, Haibo Hu, Qingqing Ye +2
Information leakage issues in machine learning-based Web applications have attracted increasing attention. While the risk of data privacy leakage has been rigorously analyzed, the…
WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching
Ronghua Li, Shinan Liu, Haibo Hu +2
IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices a…
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Yaxin Xiao, Qingqing Ye, Li Hu +5
Machine unlearning enables the removal of specific data from ML models to uphold the right to be forgotten. While approximate unlearning algorithms offer efficient alternatives to…
How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework
Zi Liang, Liantong Yu, Shiyu Zhang +2
Overestimation in evaluating large language models (LLMs) has become an increasing concern. Due to the contamination of public benchmarks or imbalanced model training, LLMs may ach…
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
Zi Liang, Haibo Hu, Qingqing Ye +2
Low rank adaptation (LoRA) has emerged as a prominent technique for fine-tuning large language models (LLMs) thanks to its superb efficiency gains over previous methods. While exte…
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
Xiaoyu Xu, Xiang Yue, Yang Liu +5
Unlearning in large language models (LLMs) aims to remove specified data, but its efficacy is typically assessed with task-level metrics like accuracy and perplexity. We show that…
OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
Xiaoyu Xu, Minxin Du, Qingqing Ye +1
Large language models (LLMs) trained over extensive corpora risk memorizing sensitive, copyrighted, or toxic content. To address this, we propose \textbf{OBLIVIATE}, a robust unlea…
From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy
Qingqing Ye, Liantong Yu, Kai Huang +3
Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the origin…
Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy
Rong Du, Qingqing Ye, Yaxin Xiao +3
Stream data from real-time distributed systems such as IoT, tele-health, and crowdsourcing has become an important data source. However, the collection and analysis of user-generat…
Multi-class Item Mining under Local Differential Privacy
Yulian Mao, Qingqing Ye, Rong Du +3
Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy (LDP) was…
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
Sen Zhang, Qingqing Ye, Haibo Hu +1
The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training…
Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services
Rong Du, Qingqing Ye, Yue Fu +1
Local Differential Privacy (LDP) has emerged as a widely adopted privacy-preserving technique in modern data analytics, enabling users to share statistical insights while maintaini…
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
Haoyang Li, Li Bai, Qingqing Ye +4
Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to re…
FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
Zitong Li, Qingqing Ye, Haibo Hu
Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying w…
Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
Jiale Zhang, Bosen Rao, Chengcheng Zhu +6
Graph Neural Networks (GNNs) have achieved remarkable performance through their message-passing mechanism. However, recent studies have highlighted the vulnerability of GNNs to bac…
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy
Sen Zhang, Qingqing Ye, Haibo Hu
Graph embedding generation techniques aim to learn low-dimensional vectors for each node in a graph and have recently gained increasing research attention. Publishing low-dimension…
PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy
Sen Zhang, Haibo Hu, Qingqing Ye +1
The objective of privacy-preserving synthetic graph publishing is to safeguard individuals' privacy while retaining the utility of original data. Most existing methods focus on gra…
Federated Heavy Hitter Analytics with Local Differential Privacy
Yuemin Zhang, Qingqing Ye, Haibo Hu
Federated heavy hitter analytics enables service providers to better understand the preferences of cross-party users by analyzing the most frequent items. As with federated learnin…