EDBT 2026 Demo / reviewers in the wild / expert
Yuen Kei Wong
dblp:433/2270
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 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.
| Network and information security
1 paper |
Privacy and data protection · 50% Usable security · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Usable security
developer-centered security |
1.0 | 1 | 2026 | PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions · CHI 2026 |
Privacy and data protection
privacy risk assessment |
1.0 | 1 | 2026 | PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions · CHI 2026 |
Human-AI interaction › AI-assisted creativity
LLM-assisted design |
0.3 | 1 | 2026 | PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
user study · 2.0observational study · 2.0large language model · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice QuestionsabstractNIST’s Privacy Risk Assessment Methodology (PRAM) provides a structured framework for privacy experts to assess privacy risks. However, its complexity and reliance on expert knowledge make it difficult for novice developers to use effectively. This paper explores methods to lower these barriers. We first performed an observational study with 12 participants using PRAM in real-world scenarios, and found that novice developers struggled most with articulating privacy-related design decisions. We then developed PrivacyAkinator, an interactive tool that helps developers articulate key privacy decisions by answering LLM-generated multiple-choice questions. PrivacyAkinator introduces three innovations: a universal privacy representation that abstracts privacy-related design decisions into data flows and stakeholder interactions; a domain-aware design space mined from 10K privacy-related news articles; and a dynamic question-generation workflow to prioritize relevant questions. Our user study with 24 participants suggests that developers using PrivacyAkinator identified 47% more key decisions in 73% less time compared to PRAM. Qiyu Li 0001, Yuen Sum Wong, Yuen Kei Wong, Longxuan Yu, Haojian Jin |
CHI | 3 |