10.1184/R1/6626354.v1
Norman Sadeh
Norman
Sadeh
Jason Hong
Jason
Hong
Lorrie Cranor
Lorrie
Cranor
Ian Fette
Ian
Fette
Patrick Kelley
Patrick
Kelley
Madhu Prabaker
Madhu
Prabaker
Jinghai Rao
Jinghai
Rao
Understanding and Capturing People’s Privacy Policies in a People Finder Application
Carnegie Mellon University
2008
Software Research
2008-01-01 00:00:00
Journal contribution
https://kilthub.cmu.edu/articles/journal_contribution/Understanding_and_Capturing_People_s_Privacy_Policies_in_a_People_Finder_Application/6626354
A number of mobile applications have
emerged that allow users to locate one another.
However, people have expressed concerns about the
privacy implications associated with this class of
software, suggesting that broad adoption may only
happen to the extent that these concerns are adequately
addressed. In this article, we report on our work on
PEOPLEFINDER, an application that enables cell phone
and laptop users to selectively share their locations
with others (e.g. friends, family, and colleagues). The
objective of our work has been to better understand
people’s attitudes and behaviors towards privacy as
they interact with such an application, and to explore
technologies that empower users to more effectively
and efficiently specify their privacy preferences (or
“policies”). These technologies include user interfaces
for specifying rules and auditing disclosures, as well as
machine learning techniques to see if the system can
help people manage their policies better. We present
evaluations of these technologies in the context of one
laboratory study and three field studies.