Papers with MLDP
CluSanT: Differentially Private and Semantically Coherent Text Sanitization (2025.naacl-long)
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| Challenge: | Existing implementations of Differential Privacy (DP) in NLP typically degrade semantic integrity and readability for humans, posing significant challenges for applications requiring high-quality, coherent text processing. |
| Approach: | They propose a text sanitization framework based on Metric Local Differential Privacy (MLDP) that uses large language models to create a set of potential substitute tokens and a parameterized cluster embedding to samaritize/substitute sensitive tokens. |
| Outcome: | The proposed framework can be tuned with parameters such that existing state-of-the-art token sanitization algorithms can be described and improved. |
A Customized Text Sanitization Mechanism with Differential Privacy (2023.findings-acl)
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| Challenge: | Existing methods to sanitize texts subject to differential privacy do not work for non-metric semantic similarity measures. |
| Approach: | They propose a customized text sanitization mechanism based on a metric local differential privacy definition. |
| Outcome: | The proposed mechanism achieves better privacy-utility trade-offs than existing mechanisms on benchmark datasets. |