Collaborative Research: SaTC: CORE: Small: Supporting Privacy Negotiation Among Multiple Stakeholders in Smart Environments

About This Grant

Internet-of-Things (IoT) devices are increasingly used in shared spaces (e.g., homes, apartments, schools, hospitals, workplaces, and cities), turning these spaces into smart environments. Different stakeholders in these environments, including both direct users of smart devices and non-users such as visitors or bystanders, have unique privacy needs and expectations. Although prior research shows evidence of conflicts among stakeholders, there has been less investigation regarding how stakeholders resolve such conflicts and negotiate their privacy options. This interdisciplinary project is investigating different stakeholders’ privacy negotiation behaviors in smart environments by designing, developing, and deploying an interactive system to collect people’s real-world privacy negotiation behaviors. The project is contributing solutions that help people manage and negotiate their privacy in various smart environments, especially when their privacy needs conflict with others’. The results will also inform privacy negotiations within other emerging technologies (e.g., virtual reality and the metaverse). This project moves beyond the lab setting to investigate and support stakeholders’ privacy negotiation behaviors in real-world smart environments. To do this, the project team is identifying contextual factors that lead to privacy concerns across multiple stakeholder groups and complex smart environments through the lens of privacy as “contextual integrity”. It is capturing stakeholders’ privacy negotiation behaviors in the real world by iteratively designing and implementing a tool that collects data on smart environmental contexts and privacy negotiation behaviors in real-world smart environments. Finally, it is developing a data-driven approach to support privacy negotiations in the real-world and evaluating its long-term impact on different stakeholder groups through field studies. The team is facilitating the future extension of this work to other new technologies by publicly sharing the anonymized dataset collected using the developed system, features of the project’s machine learning models, and a working prototype of the system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.