Award Abstract # 2426397
Collaborative Research: SaTC: CORE: Small: Empathy-Based Privacy Education and Design through Synthetic Persona Data Generation

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY
Initial Amendment Date: July 15, 2024
Latest Amendment Date: July 15, 2024
Award Number: 2426397
Award Instrument: Standard Grant
Program Manager: Sara Kiesler
skiesler@nsf.gov
 (703)292-8643
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2024
End Date: September 30, 2027 (Estimated)
Total Intended Award Amount: $199,990.00
Total Awarded Amount to Date: $199,990.00
Funds Obligated to Date: FY 2024 = $199,990.00
History of Investigator:
  • Yaxing Yao (Principal Investigator)
    yaxing@jhu.edu
Recipient Sponsored Research Office: Virginia Polytechnic Institute and State University
300 TURNER ST NW
BLACKSBURG
VA  US  24060-3359
(540)231-5281
Sponsor Congressional District: 09
Primary Place of Performance: Virginia Polytechnic Institute and State University
620 Drillfield Drive
BLACKSBURG
VA  US  24061-1050
Primary Place of Performance
Congressional District:
09
Unique Entity Identifier (UEI): QDE5UHE5XD16
Parent UEI: X6KEFGLHSJX7
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7434, 025Z, 7923
Program Element Code(s): 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070, 47.075

ABSTRACT

Privacy is often perceived as an abstract concept by both internet users and software developers. When users are engaged in online activities, it is difficult for them to make informed decisions about their personal data due to the challenges they face in understanding and experiencing the privacy implications of their behaviors in advance. Similarly, many software developers lack the ability to comprehend how the data practices of their applications may impact user privacy and to implement proper data practices that conform to users? privacy expectations. This project is tackling this problem by developing a new, empathy-based framework to enhance privacy education and design. The project team is using generative AI to create synthetic personas with AI-generated personal data. Using the personas, the team is designing, creating, and studying new interactive sandboxes and developer tools that allow individuals to empathize with these personas, leading to a more concrete and situated understanding of privacy. This understanding, in turn, fosters positive privacy-oriented behaviors among internet users and privacy-responsible software development practices among software developers.

To enhance users? privacy knowledge and developers? privacy-responsible software development practices, the project is systematically studying the mechanisms and applications of empathy invocation in the context of privacy. The goal is to develop metrics, guidelines, and conceptual frameworks for empathy-based approaches that foster privacy and security in cyberspace. Using these findings, the project team is employing user-centered design methods to develop: 1) systems that invoke empathy to improve users? privacy literacy and decision-making; and 2) empathy-based developer tools that support developers to proactively identify and address diverse privacy needs of users at the early stages of the development life cycle. These systems are deployed in outreach events to promote privacy literacy in under-resourced user and developer communities. Additionally, they are incorporated into college-level privacy literacy educational modules to support hands-on experiential learning.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chen, Chaoran and Zhou, Daodao and Ye, Yanfang and Li, Toby Jia-Jun and Yao, Yaxing "CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications" , 2025 https://doi.org/10.1145/3708359.3712156 Citation Details
Liu, Lanjing and Wang, Xiaozheng and Hasan, Shaddi and Yao, Yaxing "Co-Design Privacy Notice and Controls with Children" , 2025 https://doi.org/10.1145/3706599.3719886 Citation Details
Liu, Lanjing and Yao, Yaxing "From Knowledge to Practice: Co-Designing Privacy Controls with Children" , 2025 https://doi.org/10.1145/3706598.3713257 Citation Details
Wang, Xingyi and Wang, Xiaozheng and Park, Sunyup and Yao, Yaxing "Mental Models of Generative AI Chatbot Ecosystems" , 2025 https://doi.org/10.1145/3708359.3712125 Citation Details
Wen, Jinhe and Zhao, Yingxi and Xu, Wenqian and Yao, Yaxing and Jin, Haojian "Teaching Data Science Students to Sketch Privacy Designs Through Heuristics" , 2025 https://doi.org/10.1109/SP61157.2025.00147 Citation Details
Wen, Zikai and Liu, Lanjing and Yao, Yaxing "Supporting Family Discussions About Digital Privacy Through Perspective-Taking: An Empirical Investigation" , 2025 https://doi.org/10.1109/SP61157.2025.00149 Citation Details

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