Haibo Hu

Mobius Injection: From One-Shot Prompt Injection to Sustained Resource Exhaustion in AI Agent Infrastructure

Zi Liang, Ronghua Li, Yanyun Wang +2

Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digital services and LLM infrastruc…

Grounding-Driven Attack: Improving Encoder-based Adversarial Transferability against Large Vision-Language Models

Xinwei Zhang, Li Bai, Tianwei Zhang +5

Large vision-language models (LVLMs) have achieved impressive performance across multimodal tasks, but their reliance on visual inputs exposes them to adversarial threats. Encoder-…

United We Defend: Collaborative Membership Inference Defenses in Federated Learning

Li Bai, Junxu Liu, Sen Zhang +3

Membership inference attacks (MIAs), which determine whether a specific data point was included in the training set of a target model, have posed severe threats in federated learni…

On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression

Xinwei Zhang, Hangcheng Liu, Li Bai +4

Visual token compression is widely used to accelerate large vision-language models (LVLMs) by pruning or merging visual tokens, yet its adversarial robustness remains unexplored. W…

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…

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…

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…

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…

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…

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…

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…