Wutschitz, Lukas

Differentially Private Fine-tuning of Language Models

Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al. (2021) · arXiv

Privacy-Preserving Technologies in DataTopic Modeling

Cited by 101

Analyzing Information Leakage of Updates to Natural Language Models

Zanella-Béguelin, S., Wutschitz, L., Tople, S., Rühle, V., Paverd, A., Ohrimenko, O., Köpf, B., & Brockschmidt, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 86

Securing AI Agents with Information-Flow Control

Costa, M., Köpf, B., Kolluri, A., Paverd, A., Russinovich, M., Salem, A., Tople, S., Wutschitz, L., & Zanella-Béguelin, S. (2025) · arXiv

Security and Verification in ComputingAccess Control and Trust

Cited by 6

ACON: Optimizing Context Compression for Long-horizon LLM Agents

Kang, M., Chen, W.-N., Han, D., Inan, H. A., Wutschitz, L., Chen, Y., Sim, R., & Rajmohan, S. (2025) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 1

Permissive Information-Flow Analysis for Large Language Models

Siddiqui, S. A., Gaonkar, R., Köpf, B., Krueger, D., Paverd, A., Salem, A., Tople, S., Wutschitz, L., Xia, M., & Zanella-Béguelin, S. (2024) · arXiv

Security and Verification in ComputingAdversarial Robustness in Machine Learning

Cited by 0