Empowering developers to build privacy-compliant IoT applications

Aljeraisy, Atheer 2026. Empowering developers to build privacy-compliant IoT applications. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Internet of Things (IoT) applications handle sensitive data, requiring compliance with privacy and data protection laws. However, developers face challenges due to the vagueness of regulations and their complex implementation. Despite frameworks like Privacy by Design (PbD) and regulations such as the General Data Protection Regulation (GDPR), translating these guidelines into actionable practices in IoT environments remains difficult. The lack of practical tools and educational resources exacerbates these issues, leading to misuse and non-compliance. To address this, we analysed global privacy laws, including the GDPR, Personal Information Protection and Electronic Documents Act (PIPEDA), California Consumer Privacy Act (CCPA), Australian Privacy Principles (APPs), and New Zealand’s Privacy Act, creating a Combined Privacy Laws Framework (CPLF) to help developers ensure compliance across jurisdictions. Since PbD schemes serve as mechanisms to operationalise these laws, we mapped them to the CPLF’s principles and rights, bridging the gap between legal mandates and software implementation while demonstrating the applicability of PbD patterns to IoT architectures. Building on this, we conducted a focus group study with developers to explore PbD schemes’ applicability in End-User Development (EUD) environments. We propose Canella, an integrated IoT development ecosystem augmented with privacy components. Canella uses Blockly@rduino and Node-RED to help developers integrate privacy during development and prototyping, providing feedback on privacy concerns. Lab studies validated its effectiveness in promoting privacy-preserving development, improving compliance, and reducing cognitive load. Developers found Canella user-friendly and effective in raising privacy awareness. We further enhanced Canella’s educational capabilities by integrating widely taught privacy techniques, aligning them with PbD guidelines and CPLF. Focus group studies informed the design of these techniques and educational features. Using a real-world IoT scenario, we demonstrated how Canella can foster privacy-conscious development and enrich learning

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date of First Compliant Deposit:	29 July 2026
Date of Acceptance:	17 July 2026
Last Modified:	03 Aug 2026 11:06
URI:	https://orca.cardiff.ac.uk/id/eprint/188600

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