2 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
"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…