activity
20242026
most citedThe Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization

2 citations · 7 across the 20 of their papers we have counts for

collaborators
Showing cs.CLShow all

17 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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,…

cs.CL2025

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…