2 citations · 7 across the 24 of their papers we have counts for
25 papers
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…
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…
A Case Study on the Impact of Anonymization Along the RAG Pipeline
Andreea-Elena Bodea, Stephen Meisenbacher, Florian Matthes
Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported utility of RAG-enabled insights…
Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice
Anxhela Maloku, Alexandra Klymenko, Stephen Meisenbacher +1
In the study of Human-Computer Interaction, privacy is often seen as a core issue, and it has been explored directly in connection with User Interface (UI) and User Experience (UX)…
SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems
Andreea-Elena Bodea, Stephen Meisenbacher, Alexandra Klymenko +1
The continued promise of Large Language Models (LLMs), particularly in their natural language understanding and generation capabilities, has driven a rapidly increasing interest in…
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…
"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
Alexandra Klymenko, Stephen Meisenbacher, Patrick Gage Kelley +3
The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe…
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…
The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization
Stephen Meisenbacher, Alexandra Klymenko, Andreea-Elena Bodea +1
Differentially private text sanitization refers to the process of privatizing texts under the framework of Differential Privacy (DP), providing provable privacy guarantees while al…
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…
Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting
Stephen Meisenbacher, Chaeeun Joy Lee, Florian Matthes
The task of is a class of text privatization techniques in which (sensitive) input textual documents are under…
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…
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.…