Findings & Technologies

The following is a brief summary of findings and technologies developed so far:

• People’s location privacy preferences are complex and diverse
• People have a hard time articulating their location privacy preferences, especially in the context of usage scenarios that are new to them or that they do not understand well
• New techniques influenced by recent research in mechanism design can be used to quantify the benefits associated with exposing different combinations of privacy settings to users
• These same techniques help explain why location sharing applications deployed so far have experienced difficulty gaining broad adoption: their privacy settings make it difficult to capture many of the situations where people would actually be willing to selectively disclose their locations (e.g. to friends, colleagues, or businesses)
• While people’s location privacy preferences are complex and diverse, user-oriented machine learning techniques can be used to derive a small set of relatively simple privacy personas that can help reduce user burden when it comes to configuring one’s privacy settings
• Auditing interfaces and user-oriented machine learning techniques can motivate and help users refine their privacy settings over time
• Privacy interfaces currently available on mobile operating systems such as iOS and Android fall short when it comes to empowering users to make informed decisions about the disclosure of their locations