Human-Centered Design of Private Computation

March 16, 2023
3:00-4:00pm ET
Cummings 270
Speaker: Bailey Kacsmar
Host: Diane Souvaine


Data is not just a convenient abstraction for a collection of values, but something meaningful that connects back to actual people; people whose lives can be impacted by companies using their data. Private computations for data analysis, such as private set intersection and private machine learning, have the potential to balance individuals’ privacy rights and companies’ economic goals. However, private computation is not a simple or seemingly magical solution to the complex space that is company data sharing practices. Private computation must inspire trust and communicate how it can match the expectations of the data subjects to earn their meaningful consent to the use of their data in such computations. In this talk, I present my work on the development of privacy enhanced data analysis techniques, the implications of my work on user perspectives of the space, and my ongoing work towards the development of accessible human-centered private data analysis techniques and protocols.


Bailey Kacsmar is a PhD candidate in the School of Computer Science at the University of Waterloo. Her research interests are in the development of user-conscious privacy-enhancing technologies through the parallel study of technical approaches for private computation alongside the corresponding user perceptions, concerns, and comprehension of these technologies. Her work has been published in venues such as USENIX Security and the Privacy Enhancing Technologies Symposium (PETS).

She received a Masters' degree in Computer Science from the University of Waterloo. She has served as the PETS artifact co-chair (2022, 2023) in addition to reviewing for and serving on the program committee of security and privacy venues, human computer interaction venues, and computer science education venues (PETS, ACM CCS, ACM CHI, ACM ITiCSE). Bailey is a recipient of several scholarships, including an NSERC Doctoral Scholarships (CGS D), a Cheriton Scholarship and the Cybersecurity and Privacy Excellence Graduate Scholarship.