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Vincent Freiberger

dblp:365/4067 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0000-8549-9119ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › privacy
privacy policy comprehension
1.012026
Helping Johnny Make Sense of Privacy Policies with LLMs · CHI 2026
Privacy and data protection › privacy policy
privacy policy analysis
0.312026
Helping Johnny Make Sense of Privacy Policies with LLMs · CHI 2026

Methods — techniques the papers use, named apart from their topics

retrieval-augmented generation · 2.0large language model · 2.0
YearPublicationVenuePosition
2026 Helping Johnny Make Sense of Privacy Policies with LLMs
abstract
Understanding and engaging with privacy policies is crucial for online privacy, yet these documents remain notoriously complex and difficult to navigate. We present PRISMe, an interactive browser extension that combines LLM-based policy assessment with a dashboard and customizable chat interface, enabling users to skim quick overviews or explore policy details in depth while browsing. We conduct a user study (N=22) with participants of diverse privacy knowledge to investigate how users interpret the tool’s explanations and how it shapes their engagement with privacy policies, identifying distinct interaction patterns. Participants valued the clear overviews and conversational depth, but flagged some issues, particularly adversarial robustness and hallucination risks. Thus, we investigate how a retrieval-augmented generation (RAG) approach can alleviate issues by re-running the chat queries from the study. Our findings surface design challenges as well as technical trade-offs, contributing actionable insights for developing future user-centered, trustworthy privacy policy analysis tools.
Vincent Freiberger, Arthur Fleig, Erik Buchmann
CHI1