Empowering IoT Developers with Privacy-Preserving End-User Development Tools
Atheer Aljeraisy, Omer F. Rana, Charith Perera
Abstract
Internet of Things applications (IoT) have the potential to derive sensitive user data, necessitating adherence to privacy and data protection laws. However, developers often struggle with privacy issues, resulting in personal data misuse. Despite the proposed Privacy by Design (PbD) approach, criticism arises due to its ambiguity and lack of practical tools for educating software engineers. We introduce Canella, an integrated IoT development ecosystem with privacy-preserving components leveraging End-User Development (EUD) tools Blockly@rduino and Node-RED, to help developers build end-to-end IoT applications that prioritize privacy and comply with regulations. It helps developers integrate privacy during the development process and rapid prototyping phases, offering real-time feedback on privacy concerns. We start by conducting a focus group study to explore the applicability of designing and implementing PbD schemes within different development environments. Based on this, we implemented a proof-of-concept prototype of Canella and evaluated it in controlled lab studies with 18 software developers. The findings reveal that developers using Canella created more privacy-preserving applications, gained a deeper understanding of personal data management, and achieved better privacy compliance. Our results also highlight Canella's role in educating and promoting privacy awareness, enhancing productivity, streamlining privacy implementation, and significantly reducing cognitive load. Overall, developers found Canella and its privacy-preserving components useful, easy to use, and easy to learn, which could potentially improve IoT application privacy. Watch the demo video.
CCS Concepts: • Security and privacy → Usability in security and privacy; • Human-centered computing → Empirical studies in ubiquitous and mobile computing; • Software and its engineering → Integrated and visual development environments.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1a8885b5-ad7a-4518-be13-a99c4ea644b8Builds on4
- Privacy Champions in Software Teams: Understanding Their Motivations, Strategies, and ChallengesMohammad Tahaei, Alisa Frik, Kami VanieaCHI 2021 · 75 citations
- How Developers Talk About Personal Data and What It Means for User Privacy: A Case Study of a Developer Forum on RedditTianshi Li, Elizabeth Louie, Laura Dabbish, Jason I. HongCSCW 2020 · 64 citations
- Honeysuckle: Annotation-Guided Code Generation of In-App Privacy NoticesTianshi Li, Elijah B. Neundorfer, Yuvraj Agarwal, Jason I. HongUbiComp 2021 · 18 citations
- PARROT: Interactive Privacy-Aware Internet of Things Application Design ToolNada Alhirabi, Stephanie Beaumont, Jose Tomas Llanos, Dulani Meedeniya et al.UbiComp 2023 · 8 citations
Related papers
- A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of ThingsYuanyuan Feng, Yaxing Yao, Norman M. SadehCHI 2021 · 114 citations
- Informing the Design of a Personalized Privacy Assistant for the Internet of ThingsJessica Colnago, Yuanyuan Feng, Tharangini Palanivel, Sarah Pearman et al.CHI 2020 · 103 citations
- "We are a startup to the core": A qualitative interview study on the security and privacy development practices in Turkish software startupsDilara Keküllüoglu, Yasemin AcarS&P 2023
- IoTFlow: Inferring IoT Device Behavior at Scale through Static Mobile Companion App AnalysisDavid Schmidt, Carlotta Tagliaro, Kevin Borgolte, Martina LindorferCCS 2023 · 13 citations
- Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSAIvan De Oliveira Nunes, Seoyeon Hwang, Sashidhar Jakkamsetti, Gene TsudikS&P 2022 · 11 citations
