24 citations · 56 across the 31 of their papers we have counts for
22 papers · 1 filter
DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Texts
Ibrahim Baroud, Stephen Meisenbacher, Sebastian Möller +2
Despite the strengths of modern anonymization and de-identification techniques, the risk of re-identification remains significant due to the indirect identifiers remaining in texts…
PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization
Stephen Meisenbacher, Andreea-Elena Bodea, Ahmet Bilal Akın +3
Natural Language Processing methods have enabled novel solutions and advances in the field of privacy, particularly in the sub-domain of text-to-text privatization, where the goal…
Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy
Stephen Meisenbacher, Vlad Garbuz, Chirill Donos +5
Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and robust automatic hate speech…
Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Stephen Meisenbacher, Peter Norlander
Utilizing LLMs for automated taxonomy construction presents a clear opportunity for the comprehensive, yet efficient mapping of potentially complex domains. When contending with hi…
A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
Stephen Meisenbacher, Angelo Kleinert, Florian Matthes
The goal of differentially private text obfuscation is to obfuscate, or "perturb", input texts with Differential Privacy (DP) guarantees, such that the private output texts are qua…
With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting
Stephen Meisenbacher, Florian Matthes
Recent work in Differential Privacy with Natural Language Processing (DP NLP) has proposed numerous promising techniques in the form of text rewriting mechanisms. In the evaluation…
Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed i…
LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data
Stephen Meisenbacher, Alexandra Klymenko, Florian Matthes
Despite advances in the field of privacy-preserving Natural Language Processing (NLP), a significant challenge remains the accurate evaluation of privacy. As a potential solution,…
When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
Mahdi Dhaini, Stephen Meisenbacher, Ege Erdogan +2
In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of explainability and privacy. While research inte…
Investigating User Perspectives on Differentially Private Text Privatization
Stephen Meisenbacher, Alexandra Klymenko, Alexander Karpp +1
Recent literature has seen a considerable uptick in (DP NLP). This includes DP text privatization, where potentially s…
Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutes
Juraj Vladika, Stephen Meisenbacher, Florian Matthes
Lexical Substitution is the task of replacing a single word in a sentence with a similar one. This should ideally be one that is not necessarily only synonymous, but also fits well…
On the Impact of Noise in Differentially Private Text Rewriting
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
The field of text privatization often leverages the notion of (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual…
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…
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…
Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)
Stephen Meisenbacher, Peter Norlander
We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche mac…
Differential Privacy in Natural Language Processing: The Story So Far
Oleksandra Klymenko, Stephen Meisenbacher, Florian Matthes
As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order…