PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions

TL;DR

PrivacyAkinator is an interactive tool that simplifies privacy risk assessment for developers by guiding them through key privacy decisions using AI-generated questions, improving efficiency and decision coverage.

Contribution

It introduces a universal privacy representation, domain-aware design space, and dynamic question-generation to assist developers in articulating privacy decisions effectively.

Findings

Developed PrivacyAkinator to aid privacy decision articulation.

Users identified 47% more key decisions.

Time to identify decisions reduced by 73%.

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. 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.