EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy-Preserving Mobile Data Collection for Social and Behavioral Research

社会和行为研究在收集数字化数据方面有着悠久的历史。随着越来越多的研究纳入参与者的智能手机传感器和使用数据，个人隐私已成为一个严重的问题。尽管研究人员接受过保密训练，但可以从智能手机上有关参与者及其环境的数据中推断出敏感信息（例如，家庭和社区）。如果没有系统的解决方案来解决隐私问题，参与者可能会退出并要求从研究中删除其数据。 该项目旨在为研究参与者在数据收集期间提供隐私保护，从而鼓励研究参与。 此外，该项目还展示了在尊重参与者隐私偏好的同时可以获得有意义的研究结果。该项目通过集中关注隐私技术的有用性和局限性，将隐私增强技术社区与社会和行为研究社区联系起来。具体而言，该项目（1）包括对通常收集的智能手机数据类型的隐私保护方法进行系统审查，并开发与广泛采用的研究平台兼容的开源工具;（2）通过纳入社会和行为研究人员的观点，研究数据隐私方法的有用性，并确定局限性和开放性挑战;以及（3）通过对难民职业行为的案例研究，进一步研究参与者的隐私关注和隐私增强数据的效用，该公司专注于使用调查和移动的行为数据来预测工作，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.