Papers by Stephen Meisenbacher
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting (2024.emnlp-main)
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| Challenge: | Recent studies have focused on the integration of Differential Privacy (DP) into NLP techniques. |
| Approach: | They propose a method for text privatization leveraging language models to rewrite texts . they examine the usability of DP in NLP and its benefits over non-DP approaches . |
| Outcome: | The proposed method is a novel method for text privatization leveraging language models to rewrite texts. |
On the Impact of Noise in Differentially Private Text Rewriting (2025.findings-naacl)
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| Challenge: | a field of text privatization often requires the addition of noise to vector representations of text . however, noise addition leads to considerable utility loss, highlighting one drawback of DP in NLP. |
| Approach: | They propose a sentence-infilling privatization technique that adds noise to vector representations of text to provide privacy guarantees. |
| Outcome: | The proposed method shows that non-DP privatization methods excel in utility preservation and can find an acceptable privacy-utility trade-off, but cannot outperform DP methods in empirical privacy protections. |
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models (2024.findings-acl)
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| Challenge: | Existing methods for text privatization using Differential Privacy rely on autoregressive models which lack a mechanism to contextualize the private rewriting process. |
| Approach: | They propose a method for differentially private text rewriting using masked language models to rewrite a text one token at a time. |
| Outcome: | The proposed method preserves utility at lower levels, compared to previous methods relying on autoregressive models with a decoder. |
Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees (2025.emnlp-main)
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| Challenge: | Existing studies on differential privacy in text privatization use word perturbations and rewriting methods to protect privacy. |
| Approach: | They propose a method which leverages semantic triples for neighborhood-aware private document generation under local DP guarantees. |
| Outcome: | The proposed method allows for coherent text generation even at lower values while still balancing privacy and utility. |