Project Overview

Builds an AI-assisted tool that helps novice software engineers learn privacy-by-design practices and legal compliance through guided design activities.

This project addresses privacy education gaps for novice software engineers in IoT development. Privacy tends to be overlooked in IoT development due to system complexity, and universities often emphasize cybersecurity over privacy instruction.

The tool highlights privacy risks in IoT system designs, recommends suitable privacy-preserving measures, and exposes learners to relevant legal frameworks. The project encapsulates 168 privacy-preserving measures (ranging from high-level principles to implementation-specific patterns) into a unified learning platform. Literature reviews and user evaluations inform the AI-driven tutorial experience.

Research Objectives:

• Conduct a literature review on intelligent design tools and their educational effectiveness
• Develop techniques highlighting privacy risks and recommending contextual privacy-preserving measures
• Evaluate proposed techniques for efficiency, effectiveness, usability, and scalability

Team

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