Theses Bachelor's

Predictive Privacy: Modeling Privacy Harms

Radway, Sarah

This work examines how we can account for harm arising from innovation in machine learning and database matching, and will propose a prefatory method for the quantification of harm. The work’s primary contribution is to propose a method of modeling the harm of a dataset, analyzing the risk of deanonymization given these new factors. We carry out two experiments in order to demonstrate the harm accompanying modern dataset applications -- the first exploring machine learning's applications to WhatsApp social network analysis, the second exploring the potential for large-scale database matching between University directories and TinderU profiles. We apply our model to these examples, to demonstrate its the efficacy in a real world setting. We show that this model can be applied as a general framework, to guide both legal regulation of data release and implementation of current anonymization methods.


More About This Work

Academic Units
Computer Science
Thesis Advisors
Bellovin, Steven Michael
B.A., Columbia University
Published Here
September 7, 2021