用AI问答工具帮开发者快速理清隐私设计关键决策
PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions
- 通过LLM生成选择题引导开发者回答隐私相关设计问题
- 使用该工具后，开发者发现的隐私决策多47%，耗时减少73%
- 适合缺乏隐私经验的开发人员快速上手隐私风险评估
NIST的隐私风险评估方法（PRAM）为隐私专家提供了结构化框架，但其复杂性和对专业知识的依赖使新手开发者难以有效使用。本研究首先通过对12名参与者在真实场景中使用PRAM的观察发现，新手最困难的是明确隐私相关的设计决策。为此，我们开发了PrivacyAkinator——一个交互式工具，通过回答由大语言模型生成的选择题，帮助开发者阐明关键隐私决策。该工具包含三项创新：统一的隐私表示法，将隐私设计决策抽象为数据流与利益相关者互动；从10,000篇隐私相关新闻中挖掘出领域感知的设计空间；以及动态问题生成流程，优先呈现相关问题。24名参与者的用户研究表明，使用PrivacyAkinator的开发者比使用PRAM多识别47%的关键决策，且耗时减少73%。
原文摘要 · Abstract (English)
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. 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.
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