Understanding and capturing people’s privacy policies in a mobile social networking application

By Norman M. Sadeh, Jason I. Hong, Lorrie Faith Cranor, Ian Fette, Patrick Gage Kelley, Madhu K. Prabaker, Jinghai Rao. Personal and Ubiquitous Computing, 2009

365 citations recorded by the source corpus.

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 refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

Subfields: Computer Science Applications, Sociology and Political Science, Transportation

View Paper: https://www.semanticscholar.org/paper/be9df4d41d19c70364a739f975922f2f285e3320

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Citations in the corpus

Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, background): “[52] showed that it was possible to predict people’s location sharing privacy preferences and work by Benisch et al.”
Privacy Protection for Preventing Data Over-Collection in Smart City (2016, background): “Meanwhile, due to the popularity of SNS, people form a habit of posting a photo showing what they are doing using SNS apps [28].”
Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing (2012, background): “also studied the comp lexity of people’s location sharing privacy preferences[5, 32].”
Tweets are forever: a large-scale quantitative analysis of deleted tweets (2013, background): “Prior research has shown that users care about their location privacy and the granularity at which they disclose their locations in different contexts [21, 16].”
Nudges for Privacy and Security (2017, background): “Several studies have investigated feedback in the context of location-sharing applications [Sadeh et al. 2009; Lederer et al. 2004; Tsai et al. 2009; Iachello et al. 2005; Raento and Oulasvirta 2005; Jedrzejczyk et al. 2010].”
Privacy Expectations and Preferences in an IoT World (2017, background): “They concluded that increasing people’s awareness has a critical role in helping them deﬁne more precise policies for protecting their privacy [36].”
Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain (2014, background): “Given the sensitivity of these data, for instance, records of user location, there is pressure for further regulation and ongoing work analysing the issues (Sadeh et al., 2009).”
Bridging the gap between physical location and online social networks (2010, methodology): “Locaccino [ 14 ] is a Web-application developed by the Mobile Commerce Lab at Carnegie Mellon University that allows a user to share her current location with other Locaccino users through her Facebook social network subject to user-controllable privacy rule specifications 1 . From the user’s perspe”
Counteracting the Negative Effect of Form Auto-completion on the Privacy Calculus (2013, background): “Giving people control over their disclosure decisions would thus empower them to carefully choose the level of privacy they desire (Lederer et al. 2004; Sadeh et al. 2009).”