Understanding and Capturing People’s Privacy Policies in a People Finder Application
Madhu Prabaker, Jinghai Rao, Ian Fette, Patrick Kelley, Lorrie Cranor, Jason Hong, and Norman Sadeh
Over the past few years, a number of mobile applications have emerged that allow users to locate one another. Some of these applications are driven by a desire from enterprises to increase the productivity of their employees. Others are geared towards supporting social networking scenarios or security-oriented scenarios. The growing number of cell phones sold with location tracking technologies such as GPS or A-GPS along with the emergence of WiFi-based location tracking solutions could lead to mainstream adoption of some of these applications. At the same time, however, a number of 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 work conducted at Carnegie Mellon University in the context of 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”).