Lukas Wutschitz

Principal Researcher

论文与出版物

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- Permissive Information-Flow Analysis for Large Language Models
  
  Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin
  
  Transactions on Machine Learning Research (TMLR) | October 2025
  
  Publication Publication 项目
- Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
  
  Guangchen Lan, Huseyin Inan, Sahar Abdelnabi, Janardhan (Jana) Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher G. Brinton, Robert Sim
  
  NeurIPS 2025 | May 2025
  
  DOI 项目
- Securing AI Agents with Information-Flow Control
  
  Manuel Costa, Boris Köpf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  May 2025
  
  DOI Publication Publication 项目项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
  
  February 2025
  
  DOI Preprint 项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Shruti Tople, Reza Shokri
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- Closed-Form Bounds for DP-SGD against Record-level Inference
  
  Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  USENIX Security Symposium | August 2024
  
  Publication 项目
- Bayesian Estimation of Differential Privacy
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
  
  2023 International Conference on Machine Learning | July 2023
  
  编辑: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
  
  Publication 项目
- Analyzing Leakage of Personally Identifiable Information in Language Models
  
  Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  2023 IEEE Symposium on Security and Privacy | May 2023
  
  Publication PDF DOI 项目
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication 项目
- dp-transformers: Training transformer models with differential privacy
  
  Huseyin Inan, Andre Manoel, Lukas Wutschitz
  
  July 2022
  
  Publication 项目
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication 项目
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github 项目
- Training Data Leakage Analysis in Language Models
  
  Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
  
  February 2021
  
  Preprint
- Analyzing Information Leakage of Updates to Natural Language Models
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
  
  ACM Conference on Computer and Communication Security (CCS) | November 2020
  
  Author's Version 项目

- Permissive Information-Flow Analysis for Large Language Models
  
  Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin
  
  Transactions on Machine Learning Research (TMLR) | October 2025
  
  Publication Publication 项目
- Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
  
  Guangchen Lan, Huseyin Inan, Sahar Abdelnabi, Janardhan (Jana) Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher G. Brinton, Robert Sim
  
  NeurIPS 2025 | May 2025
  
  DOI 项目
- Securing AI Agents with Information-Flow Control
  
  Manuel Costa, Boris Köpf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  May 2025
  
  DOI Publication Publication 项目项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
  
  February 2025
  
  DOI Preprint 项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Shruti Tople, Reza Shokri
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- Closed-Form Bounds for DP-SGD against Record-level Inference
  
  Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  USENIX Security Symposium | August 2024
  
  Publication 项目
- Bayesian Estimation of Differential Privacy
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
  
  2023 International Conference on Machine Learning | July 2023
  
  编辑: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
  
  Publication 项目
- Analyzing Leakage of Personally Identifiable Information in Language Models
  
  Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  2023 IEEE Symposium on Security and Privacy | May 2023
  
  Publication PDF DOI 项目
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication 项目
- dp-transformers: Training transformer models with differential privacy
  
  Huseyin Inan, Andre Manoel, Lukas Wutschitz
  
  July 2022
  
  Publication 项目
- Training Data Leakage Analysis in Language Models
  
  Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
  
  February 2021
  
  Preprint
- Analyzing Information Leakage of Updates to Natural Language Models
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
  
  ACM Conference on Computer and Communication Security (CCS) | November 2020
  
  Author's Version 项目
- Permissive Information-Flow Analysis for Large Language Models
  
  Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin
  
  Transactions on Machine Learning Research (TMLR) | October 2025
  
  Publication Publication 项目
- Securing AI Agents with Information-Flow Control
  
  Manuel Costa, Boris Köpf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  May 2025
  
  DOI Publication Publication 项目项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
  
  February 2025
  
  DOI Preprint 项目
- Closed-Form Bounds for DP-SGD against Record-level Inference
  
  Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  USENIX Security Symposium | August 2024
  
  Publication 项目
- Bayesian Estimation of Differential Privacy
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
  
  2023 International Conference on Machine Learning | July 2023
  
  编辑: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
  
  Publication 项目
- Analyzing Leakage of Personally Identifiable Information in Language Models
  
  Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  2023 IEEE Symposium on Security and Privacy | May 2023
  
  Publication PDF DOI 项目
- dp-transformers: Training transformer models with differential privacy
  
  Huseyin Inan, Andre Manoel, Lukas Wutschitz
  
  July 2022
  
  Publication 项目
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github 项目
- Training Data Leakage Analysis in Language Models
  
  Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
  
  February 2021
  
  Preprint
- Analyzing Information Leakage of Updates to Natural Language Models
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
  
  ACM Conference on Computer and Communication Security (CCS) | November 2020
  
  Author's Version 项目
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication 项目
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication 项目
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github 项目
- Training Data Leakage Analysis in Language Models
  
  Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
  
  February 2021
  
  Preprint
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github 项目

- Contextual Integrity in LLMs via Reasoning and Reinforcement Learning
  
  Guangchen Lan, Huseyin Inan, Sahar Abdelnabi, Janardhan (Jana) Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher G. Brinton, Robert Sim
  
  NeurIPS 2025 | May 2025
  
  DOI 项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Shruti Tople, Reza Shokri
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- Closed-Form Bounds for DP-SGD against Record-level Inference
  
  Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  USENIX Security Symposium | August 2024
  
  Publication 项目
- Bayesian Estimation of Differential Privacy
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
  
  2023 International Conference on Machine Learning | July 2023
  
  编辑: Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
  
  Publication 项目
- Analyzing Leakage of Personally Identifiable Information in Language Models
  
  Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  2023 IEEE Symposium on Security and Privacy | May 2023
  
  Publication PDF DOI 项目
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication 项目
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication 项目
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github 项目
- Analyzing Information Leakage of Updates to Natural Language Models
  
  Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
  
  ACM Conference on Computer and Communication Security (CCS) | November 2020
  
  Author's Version 项目
- Permissive Information-Flow Analysis for Large Language Models
  
  Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin
  
  Transactions on Machine Learning Research (TMLR) | October 2025
  
  Publication Publication 项目
- Securing AI Agents with Information-Flow Control
  
  Manuel Costa, Boris Köpf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
  
  May 2025
  
  DOI Publication Publication 项目项目
- The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
  
  Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
  
  February 2025
  
  DOI Preprint 项目
- dp-transformers: Training transformer models with differential privacy
  
  Huseyin Inan, Andre Manoel, Lukas Wutschitz
  
  July 2022
  
  Publication 项目
- Training Data Leakage Analysis in Language Models
  
  Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
  
  February 2021
  
  Preprint

Lukas Wutschitz

论文与出版物

Permissive Information-Flow Analysis for Large Language Models

Contextual Integrity in LLMs via Reasoning and Reinforcement Learning

Securing AI Agents with Information-Flow Control

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

Closed-Form Bounds for DP-SGD against Record-level Inference

Bayesian Estimation of Differential Privacy

Analyzing Leakage of Personally Identifiable Information in Language Models

Differentially Private Model Compression

dp-transformers: Training transformer models with differential privacy

Differentially private fine-tuning of language models

Numerical Composition of Differential Privacy

Training Data Leakage Analysis in Language Models

Analyzing Information Leakage of Updates to Natural Language Models

Permissive Information-Flow Analysis for Large Language Models

Contextual Integrity in LLMs via Reasoning and Reinforcement Learning

Securing AI Agents with Information-Flow Control

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

Closed-Form Bounds for DP-SGD against Record-level Inference

Bayesian Estimation of Differential Privacy

Analyzing Leakage of Personally Identifiable Information in Language Models

Differentially Private Model Compression

dp-transformers: Training transformer models with differential privacy

Training Data Leakage Analysis in Language Models

Analyzing Information Leakage of Updates to Natural Language Models

Permissive Information-Flow Analysis for Large Language Models

Securing AI Agents with Information-Flow Control

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

Closed-Form Bounds for DP-SGD against Record-level Inference

Bayesian Estimation of Differential Privacy

Analyzing Leakage of Personally Identifiable Information in Language Models

dp-transformers: Training transformer models with differential privacy

Numerical Composition of Differential Privacy

Training Data Leakage Analysis in Language Models

Analyzing Information Leakage of Updates to Natural Language Models

Differentially Private Model Compression

Differentially private fine-tuning of language models

Numerical Composition of Differential Privacy

Training Data Leakage Analysis in Language Models

Numerical Composition of Differential Privacy

Contextual Integrity in LLMs via Reasoning and Reinforcement Learning

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

Closed-Form Bounds for DP-SGD against Record-level Inference

Bayesian Estimation of Differential Privacy

Analyzing Leakage of Personally Identifiable Information in Language Models

Differentially Private Model Compression

Differentially private fine-tuning of language models

Numerical Composition of Differential Privacy

Analyzing Information Leakage of Updates to Natural Language Models

Permissive Information-Flow Analysis for Large Language Models

Securing AI Agents with Information-Flow Control

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

dp-transformers: Training transformer models with differential privacy

Training Data Leakage Analysis in Language Models