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.