PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions
The paper introduces PrivacyAkinator, a tool designed to assist developers in making key privacy design decisions. It addresses the challenges novice developers face when using NIST's Privacy Risk Assessment Methodology (PRAM). The tool reportedly enables users to identify more key decisions in significantly less time compared to traditional methods.
- ▪PrivacyAkinator helps developers articulate key privacy decisions through LLM-generated multiple-choice questions.
- ▪An observational study revealed that novice developers struggled with articulating privacy-related design decisions using PRAM.
- ▪A user study showed that participants using PrivacyAkinator identified 47% more key decisions in 73% less time than those using PRAM.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20206 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Human-Computer Interaction arXiv:2605.20206 (cs) [Submitted on 8 Apr 2026] Title:PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions Authors:Qiyu Li, Yuen Sum Wong, Yuen Kei Wong, Longxuan Yu, Haojian Jin View a PDF of the paper titled PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions, by Qiyu Li and 4 other authors View PDF HTML (experimental) Abstract:NIST'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.
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