Towards Meaningful Data Privacy Control in the Digital World
“Notice and choice” is the predominant mechanism for data privacy protection worldwide but it often fails to provide people with meaningful data privacy control. Privacy notices (e.g., privacy policies) are often ineffective to convey key privacy-related information to people because of their length and legal jargons. Also, privacy choices for people to control their data privacy are often absent, overly simplified, and sometimes manipulative. To make things worse, it requires an unrealistic amount of time and effort for people to effectively manage their data privacy. My research addresses a number of technical and usability challenges that come with “notice and choice”, with the purpose to ensure the use of people’s personal data is appropriate, fair, and meaningful in the digital world. In this talk, I will focus on my research endeavors in building usable privacy-enhancing technologies to help people take better control of their personal data privacy in the digital world, particularly within the Internet of Things (IoT) context. First, I will introduce my current work on designing and implementing a real-world privacy infrastructure that increases the transparency of IoT data privacy and empowers end-users with privacy choices in IoT. Second, I will present my applied machine learning research that models people’s privacy expectations and preferences regarding video analytics in public places. This line of my work aims to build data-driven personalized privacy assistants that help users efficiently manage their data privacy. Finally, I will touch upon my ongoing work in accessible and inclusive privacy & security, as well as my future research directions.
Dr. Yuanyuan Feng is a postdoctoral researcher in the School of Computer Science at Carnegie Mellon University and she earned her PhD from Drexel University. Her research is centered around human-computer interaction and usable privacy & security, with strong interdisciplinary expertise in privacy-enhancing technologies, ubiquitous computing, health informatics, and applied machine learning/artificial intelligence (ML/AI). She has published in top-tier computer science and privacy & security venues (e.g., ACM CHI Conference, Proceedings of Privacy Enhancing Technologies, the Web Conference), as well as prestigious information science journals (e.g., Journal of the Association for Information Science & Technology). She has two years of independent teaching experience at Drexel University and has mentored more than ten undergraduate and graduate students at Carnegie Mellon University.