2 citations · 7 across the 24 of their papers we have counts for
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Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting
Stephen Meisenbacher, Florian Matthes
The field of privacy-preserving Natural Language Processing has risen in popularity, particularly at a time when concerns about privacy grow with the proliferation of Large Languag…
An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry
Stephen Meisenbacher, Tim Schopf, Weixin Yan +2
The task of is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document…
Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives
Stephen Meisenbacher, Alexandra Klymenko, Patrick Gage Kelley +3
The rise of powerful AI models, more formally (GPAIS), has led to impressive leaps in performance across a wide range of tasks. At the same ti…
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 …
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 (…
Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text
Stephen Meisenbacher, Florian Matthes
The study of Differential Privacy (DP) in Natural Language Processing often views the task of text privatization as a task, in which sensitive input texts are…
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…
Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
Juraj Vladika, Stephen Meisenbacher, Martina Preis +2
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes.…
A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off
Stephen Meisenbacher, Nihildev Nandakumar, Alexandra Klymenko +1
The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in establ…