9 papers
Disk-Resident Graph ANN Search: An Experimental Evaluation
Xiaoyu Chen, Jinxiu Qu, Yitong Song +7
As data volumes grow while memory capacity remains limited, disk-resident graph-based approximate nearest neighbor (ANN) methods have become a practical alternative to memory-resid…
COLE: Towards Practical Column-based Learned Storage for Blockchain Systems
Ce Zhang, Cheng Xu, Haibo Hu +1
Blockchain provides a decentralized and tamper-resistant ledger for securely recording transactions across a network of untrusted nodes. While its transparency and integrity are be…
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…
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…
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…