Skip to main content
Understanding and Capturing People’s Privacy Policies in a People Finder Application
Institute for Software Research
  • Norman Sadeh, Carnegie Mellon University
  • Jason Hong, Carnegie Mellon University
  • Lorrie Faith Cranor, Carnegie Mellon University
  • Ian Fette, Carnegie Mellon University
  • Patrick Kelley, Carnegie Mellon University
  • Madhu Prabaker, Carnegie Mellon University
  • Jinghai Rao, Carnegie Mellon University
Date of Original Version
Working Paper
Rights Management
All Rights Reserved
Abstract or Description
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.
Citation Information
Norman Sadeh, Jason Hong, Lorrie Faith Cranor, Ian Fette, et al.. "Understanding and Capturing People’s Privacy Policies in a People Finder Application" (2008)
Available at: