Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University


"Sometimes Less is More":
Multi-Perspective Exploration of Disclosure Astractions
in Location-Aware Social Mobile Applications

Karen P. Tang

December 2010

Ph.D. Thesis


Keywords: Location-aware computing, location-based applications, location sharing, usable privacy, social mobile computing, information privacy, ubiquitous computing (ubicomp), context-aware computing, human-computer interaction, decision making

In the past few years, there has been increasing interest in deploying social location-sharing applications (LSAs) that enable users to continuously sense, collect, and share their location information with others. Yet, despite all the attention LSAs are receiving, studies have found that only a small percentage of mobile consumers actively use these services. One often-cited adoption barrier is that many LSAs do not adequately address end-user privacy concerns for sharing location data.

One way to address these privacy concerns is to incorporate support for disclosure abstractions in LSAs. These abstractions provide a middle-ground compromise that provides some degree of privacy protection for end-users, as well as some degree of social value to the users who are consuming the location information. In this dissertation, we look at two specific kinds of abstractions: geographic abstractions (which provide spatial blurring of one's location) and semantic abstractions (which provide obfuscation by referring to the type of location a place is, rather than by its geographical coordinates).

We present results from several studies that examine these abstractions at four different stages: how users reason about location sharing, how users configure their privacy preferences, how users interpret visual representations of their location, and what kinds of outcomes can be expected from users that share abstractions. Based on these studies, we provide empirical evidence that relatively simple privacy mechanisms like disclosure abstractions can simplify rule-based privacy configurations and increase the likelihood of location sharing, though there is still a significant chance that abstractions can be reverse-engineered. Based on qualitative user feedback, we also present several privacy implications for visualizing location information as well. By studying these issues with different types of location sharing applications as well as different user study methodologies, we provide a multi-perspective exploration of end-user privacy concerns regarding general location sharing behaviors for context-aware social mobile applications.

176 pages

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