Social and behavioral research has a long history of collecting digitized data. With more and more studies incorporating participants' smartphone sensor and usage data, individual privacy has become a serious concern. Although researchers are trained to exercise confidentiality, sensitive information can be inferred from the smartphone data about the participants and their environment (e.g., families and communities). Without a systematic solution to address privacy concerns, participants may withdraw and request removal of their data from studies. This project aims to provide privacy protection to study participants during data collection, thus encouraging research participation. Furthermore, the project demonstrates that meaningful research results can be derived while honoring the participants' privacy preferences.
The project bridges the privacy-enhancing technologies community and social and behavioral research community by focusing on the usefulness and limitations of privacy technologies, collectively. Specifically, the project (1) includes a systematic review of privacy-preserving methods for commonly collected smartphone data types and develops open-source tools compatible with widely adopted research platforms; (2) studies the usefulness of data privacy methods and identifies limitations and open challenges, by incorporating the perspectives of social and behavior researchers; and (3) further studies participant privacy concerns and the utility of privacy-enhanced data through a case study on refugee vocational behavior, which focuses on using survey and mobile behavioral data to predict job-search behaviors.
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.
