Priyanka Nanayakkara, Northwestern University
Differential privacy (DP) is a notion of privacy that has quickly achieved widespread adoption, for example by the U.S. Census Bureau, Google, Apple, Microsoft, and Uber. While DP has the potential to provide strong privacy protections, its actual guarantees depend on key implementation details, such as the privacy loss budget and deployment model (e.g., local vs. central). However, these details are seldom communicated to data subjects, limiting their ability to make informed data-sharing decisions. Therefore, to reduce the opaqueness of DP protections, we are developing portable explanations of DP.
In this talk, I will briefly describe existing strategies for explaining DP currently used in industry. Then, I will present explanations that convey the probabilistic nature of DP's guarantees and briefly touch on explanations that convey which information flows are restricted depending on the DP model used. The explanations presented can be readily employed to increase transparency around DP, and can inform communication around other privacy-enhancing technologies broadly.
This talk will be based on research conducted by Rachel Cummings, Gabriel Kaptchuk, Priyanka Nanayakkara, Elissa M. Redmiles, and Mary Anne Smart.
Priyanka Nanayakkara, Northwestern University
Priyanka Nanayakkara is a PhD candidate in computer science and communication at Northwestern University. She works at the intersection of privacy and visualization. Specifically, she designs and evaluates tools like interactive interfaces that make differential privacy usable for data curators, data analysts, and data subjects. During her PhD, Priyanka has also been a visiting researcher at Columbia University, a visiting graduate student at UC Berkeley's Simons Institute, and an intern at Microsoft Research.
author = {Priyanka Nanayakkara},
title = {Communicating Differential Privacy Guarantees to Data Subjects},
year = {2023},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = sep
}
