Faculty
2022: Busy Beaver Award für "Privacy of Machine Learning"
2019: Best paper award at NDSS
Dr. Yang Zhang ist Faculty am CISPA. Seine Forschung konzentriert sich auf Trustworthy Machine Learning (Privacy, Safety und Security). Außerdem arbeitet er an der Messung und dem Verständnis von Fehlinformationen und unsicheren Inhalten wie hasserfüllten Memes im Internet. Im Laufe der Jahre hat er zahlreiche Paper auf Spitzenkonferenzen in Informatik, einschließlich CCS, NDSS, Oakland und USENIX Security veröffentlicht. Seine Arbeit hat 2019 den NDSS Distinguished Paper Award und 2022 den CCS Best Paper Award Runner-up erhalten.
Usenix Security Symposium (USENIX-Security)
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
Usenix Security Symposium (USENIX-Security)
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Usenix Security Symposium (USENIX-Security)
Bridging the Gap in Vision Language Models in IdentifyingUnsafe Concepts Across Modalities
Usenix Security Symposium (USENIX-Security)
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD)
On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing
Annual Meeting of the Association for Computational Linguistics (ACL)
When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
Annual Meeting of the Association for Computational Linguistics (ACL)
JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
International Conference on Machine Learning (ICML)
The Ripple Effect: On Unforeseen Complications of Backdoor Attacks
IEEE International Conference on Multimedia and Expo (ICME)
Neeko: Model Hijacking Attacks Against Generative Adversarial Networks
Usenix Security Symposium (USENIX-Security)
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark