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20222024
most citedUnderstanding Privacy Risks of Embeddings Induced by Large Language Models

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2024

TDDBench: A Benchmark for Training data detection

Zhihao Zhu, Yi Yang, Defu Lian

Training Data Detection (TDD) is a task aimed at determining whether a specific data instance is used to train a machine learning model. In the computer security literature, TDD is…

cs.CL2024★ 2 cited

Understanding Privacy Risks of Embeddings Induced by Large Language Models

Zhihao Zhu, Ninglu Shao, Defu Lian +4

Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations. One promising solution to mitigate these hallucinations is to sto…

cs.CR2023★ 2 cited

Model Stealing Attack against Recommender System

Zhihao Zhu, Rui Fan, Chenwang Wu +3

Recent studies have demonstrated the vulnerability of recommender systems to data privacy attacks. However, research on the threat to model privacy in recommender systems, such as…

cs.LG2023

Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity

Zhihao Zhu, Chenwang Wu, Rui Fan +4

Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However,…

cs.LG2022

Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation

Zhihao Zhu, Chenwang Wu, Min Zhou +3

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various sa…