Hayes, Jamie

LOGAN: Membership Inference Attacks Against Generative Models

Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E. (2019) · Proceedings on Privacy Enhancing Technologies

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 433

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Naseri, M., Hayes, J., & De Cristofaro, E. (2022) · Proceedings 2022 Network and Distributed System Security Symposium

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 188

Scalable watermarking for identifying large language model outputs

Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., et al. (2024) · Nature

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 132

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Defeating Prompt Injections by Design

Debenedetti, E., Shumailov, I., Fan, T., Hayes, J., Carlini, N., Fabian, D., Kern, C., Shi, C., Terzis, A., & Tramèr, F. (2026) · 2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 15