Technology in Government (TIG) and Topics in Privacy (TIP)
11/4/2013 refreshments served at 2:30p, discussion 3 to 4pm in room K354, at 1737 Cambridge Street, Cambridge, MA 02138.
Title: Understanding the Risk of Re-identification in Behavioral Science Data
Discussant: Raquel Loran Hill, Visiting Scholar at the Center for Research on Computation and Society
Abstract:
Behavioral scientists, often collect and maintain datasets that are high-dimensional (i.e. include some combination of demographic, medical, sexual, and other personal information), and this presents opportunities to characterize participants in unique ways. The conventional wisdom for protecting the privacy of such participants is to either not ask certain questions or to remove or recode potentially identifiable information. The premise of the research discussed here is that neither approach may be sufficient for preventing the (re)identification of participants in large and/or multidimensional datasets. Per human subjects guidelines, researchers need to consider all of the potential risks including whether any disclosure of the subjects’ responses outside of the research could reasonably place the subjects at risk of criminal or civil liability or be damaging to the subjects’ financial standing, employability, insurability, or reputation .
In this work, I seek to determine whether attributes that make a participant unique within a high-dimensional social science dataset can be used to identify that individual. In addition, this work considers the possibility that new online social media (e.g., Facebook, twitter), as well as publicly available datasets (e.g., voter registration) could be used to increase the probability to identify participants.
Bio:
Raquel Hill is an Associate Professor of Computer Science in the School of Informatics and Computing. Her primary research interests are in the areas of trust and security of distributed computing environments and data privacy with a specific interest in privacy protection mechanisms for medical-related datasets. Dr. Hill’s research is funded by various sources, including the National Science Foundation. She holds B.S. and M.S. degrees in Computer Science from the Georgia Institute of Technology and a Ph.D. in Computer Science from Harvard University.