2 citations · 4 across the 10 of their papers we have counts for
10 papers
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…
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…
"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…
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,…
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…
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…
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…