2 citations · 7 across the 24 of their papers we have counts for
10 papers · 1 filter
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…