36 citations · 38 across the 31 of their papers we have counts for
14 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…
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…
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…
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…