Papers by Anthony Hughes
How Private are Language Models in Abstractive Summarization? (2025.emnlp-main)
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| Challenge: | Effective protection of private information is essential for knowledge dissemination in sensitive domains such as medical and legal. |
| Approach: | They perform a comprehensive study of privacy risks in LM-based summarization using closed- and four-weight models of different sizes and families. |
| Outcome: | The proposed models show that they leak personally identifiable information in their summaries, compared to human-generated summary summators, which show significantly higher privacy protection levels. |
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing (2026.findings-eacl)
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| Challenge: | Existing defense mechanisms to mitigate PII leakage are limited by existing defenses . a new approach, PATCH, identifies and edits PI I circuits to reduce leakage . |
| Approach: | They propose to use PATCH: Privacy-Aware Targeted Circuit Patching to identify PII leakage circuits in language models to reduce leakage. |
| Outcome: | The proposed approach reduces leakage by up to 65% and can reduce residual leakage to as low as 0.01%. |