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20242026
most citedSoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems

2 citations · 7 across the 24 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

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…

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.CR2026

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…

cs.HC2026

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)…

cs.CR2026★ 2 cited

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…