Empowering IoT Developers with Privacy-Preserving End-User Development Tools
ACM IMWUT, Volume 8, Issue 3, Article 90, August 2024.
This project investigates how privacy-by-design schemes can be mapped to legal obligations under global data protection regulations and transformed into reusable software components that integrate with mainstream IoT development tools. As IoT applications proliferate across domestic, industrial, and public environments, developers face increasing pressure to ensure their systems comply with privacy regulations such as GDPR from the outset. However, most developers lack specialised privacy expertise, and existing development frameworks provide limited support for embedding privacy protections into application logic. The research addresses this gap by analysing the relationship between privacy laws and technical privacy-by-design schemes, producing a comprehensive mapping that identifies which engineering practices satisfy specific regulatory requirements. These mappings are then operationalised as reusable privacy-preserving components that can be incorporated into end-user development environments, including visual programming platforms commonly used in IoT prototyping.
To encourage adoption, the project employs gamification techniques that award developers who embed privacy-preserving components during the build process. This approach creates positive incentives for privacy-conscious development practices, making compliance with data protection regulations a natural and rewarding part of the IoT application development workflow rather than an afterthought.
The Canella platform, developed as part of this research, provides a privacy-aware end-to-end integrated IoT development ecosystem. The project contributes to the PETRAS National Centre of Excellence for IoT Systems Cybersecurity and has produced publications addressing developer perspectives on privacy laws, end-user development tools for privacy, and integrated IoT development ecosystems.
ACM IMWUT, Volume 8, Issue 3, Article 90, August 2024.
IEEE PerCom Workshops, 2023, pp. 279–281.
ACM Computing Surveys (CSUR), Volume 54, Issue 5, June 2021.