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cs.CL2024★ 1 cited
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of …
cs.CL2024
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
Stephen Meisenbacher, Maulik Chevli, Juraj Vladika +1
The task of text privatization using Differential Privacy has recently taken the form of , in which an input text is obfuscated via the use of generative (…
cs.CL2024★ 1 cited
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
The study of privacy-preserving Natural Language Processing (NLP) has gained rising attention in recent years. One promising avenue studies the integration of Differential Privacy…