Gong, Neil Zhenqiang

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Cao, X., Fang, M., Liu, J., & Gong, N. Z. (2021) · Proceedings 2021 Network and Distributed System Security Symposium

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 999

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 828

On the Feasibility of Internet-Scale Author Identification

Narayanan, A., Paskov, H., Gong, N. Z., Bethencourt, J., Stefanov, E., Shin, E. C. R., & Song, D. (2012) · 2012 IEEE Symposium on Security and Privacy

Authorship Attribution and ProfilingUser Authentication and Security Systems

Cited by 261

TrustLLM: Trustworthiness in Large Language Models

Huang, Y., Sun, L., Wang, H., Wu, S., Zhang, Q., Li, Y., Gao, C., Huang, Y., Lyu, W., Zhang, Y., et al. (2024) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 256

Backdoor Attacks to Graph Neural Networks

Zhang, Z., Jia, J., Wang, B., & Gong, N. Z. (2021) · Proceedings of the 26th ACM Symposium on Access Control Models and Technologies

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 220

MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

Cao, X., & Gong, N. Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 205

PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Zhu, K., Wang, J., Zhou, J., Wang, Z., Chen, H., Wang, Y., Yang, L., Ye, W., Zhang, Y., Gong, N. Z., et al. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 153

PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Zhu, K., Wang, J., Zhou, J., Wang, Z., Chen, H., Wang, Y., Yang, L., Ye, W., Zhang, Y., Gong, N., et al. (2023) · Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 151

Provably Secure Federated Learning against Malicious Clients

Cao, X., Jia, J., & Gong, N. Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 141

Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference

Liu, B., Kong, D., Cen, L., Gong, N. Z., Jin, H., & Xiong, H. (2015) · Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Recommender Systems and TechniquesMobile and Web Applications

Cited by 98

Optimization-based Prompt Injection Attack to LLM-as-a-Judge

Shi, J., Yuan, Z., Liu, Y., Huang, Y., Zhou, P., Sun, L., & Gong, N. Z. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 70

PLeak: Prompt Leaking Attacks against Large Language Model Applications

Hui, B., Yuan, H., Gong, N., Burlina, P., & Cao, Y. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 55

Poisoning Federated Recommender Systems with Fake Users

Yin, M., Xu, Y., Fang, M., & Gong, N. Z. (2024) · Proceedings of the ACM Web Conference 2024

Privacy-Preserving Technologies in DataRecommender Systems and Techniques

Cited by 27

Formalizing and Benchmarking Prompt Injection Attacks and Defenses

Liu, Y., Jia, Y., Geng, R., Jia, J., & Gong, N. Z. (2023) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 14

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Huang, Y., Shi, J., Li, Y., Fan, C., Wu, S., Zhang, Q., Liu, Y., Zhou, P., Wan, Y., Gong, N. Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Prompt Injection Attack to Tool Selection in LLM Agents

Shi, J., Yuan, Z., Tie, G., Zhou, P., Gong, N. Z., & Sun, L. (2025) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 2

Self-Cognition in Large Language Models: An Exploratory Study

Chen, D., Shi, J., Wan, Y., Zhou, P., Gong, N. Z., & Sun, L. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1