36 citations · 40 across the 48 of their papers we have counts for
9 papers · 1 filter
Data Poisoning Attacks to Local Differential Privacy Protocols for Graphs
Xi He, Kai Huang, Qingqing Ye +1
Graph analysis has become increasingly popular with the prevalence of big data and machine learning. Traditional graph data analysis methods often assume the existence of a trusted…
Membership Inference Attacks and Defenses in Federated Learning: A Survey
Li Bai, Haibo Hu, Qingqing Ye +3
Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devi…
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation
Zi Liang, Qingqing Ye, Yanyun Wang +5
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extracti…
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy
Leixia Wang, Qingqing Ye, Haibo Hu +1
Answering range queries in the context of Local Differential Privacy (LDP) is a widely studied problem in Online Analytical Processing (OLAP). Existing LDP solutions all assume a u…
Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models
Zi Liang, Haibo Hu, Qingqing Ye +2
The drastic increase of large language models' (LLMs) parameters has led to a new research direction of fine-tuning-free downstream customization by prompts, i.e., task description…
PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy
Yulian Mao, Qingqing Ye, Haibo Hu +2
Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringem…
Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach
Yue Fu, Qingqing Ye, Rong Du +1
With the exponential growth of data and its crucial impact on our lives and decision-making, the integrity of data has become a significant concern. Malicious data poisoning attack…
LDPRecover: Recovering Frequencies from Poisoning Attacks against Local Differential Privacy
Xinyue Sun, Qingqing Ye, Haibo Hu +4
Local differential privacy (LDP), which enables an untrusted server to collect aggregated statistics from distributed users while protecting the privacy of those users, has been wi…