Papers by Pulkit Madaan
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains (2024.findings-emnlp)
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| Challenge: | a lack of anonymization of sensitive text data hinders development of NLP tools . poorly anonymized sensitive data cannot be easily shared with annotators or external researchers . |
| Approach: | They propose to use synthetic data to generate differentially private language models in place of real data to facilitate NLP development without compromising privacy. |
| Outcome: | The proposed model can be used to train public models without compromising privacy. |
Multi-Token Completion for Text Anonymization (2026.eacl-long)
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| Challenge: | Text anonymization is a critical task for enabling research and development in high-stakes domains containing private data. |
| Approach: | They propose a method for predicting replacements for sensitive spans with principled use-inspired evaluation criteria. |
| Outcome: | The proposed method produces more realistic text and preserves utility than alternative infilling methods and differentially private mechanisms across multiple domains without retraining. |