Who wants to know what when? privacy preference determinants in ubiquitous computing
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Developing privacy guidelines for social location disclosure applications and services
SOUPS '05 Proceedings of the 2005 symposium on Usable privacy and security
A study on the value of location privacy
Proceedings of the 5th ACM workshop on Privacy in electronic society
Privacy in Location-Aware Computing Environments
IEEE Pervasive Computing
You've been warned: an empirical study of the effectiveness of web browser phishing warnings
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding and capturing people's privacy policies in a mobile social networking application
Personal and Ubiquitous Computing
Matrix completion from a few entries
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Crying wolf: an empirical study of SSL warning effectiveness
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
When are users comfortable sharing locations with advertisers?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The effectiveness of application permissions
WebApps'11 Proceedings of the 2nd USENIX conference on Web application development
Proceedings of the 13th international conference on Ubiquitous computing
It's all about the benjamins: an empirical study on incentivizing users to ignore security advice
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Measuring user confidence in smartphone security and privacy
Proceedings of the Eighth Symposium on Usable Privacy and Security
Location privacy revisited: factors of privacy decisions
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Automatic mediation of privacy-sensitive resource access in smartphone applications
SEC'13 Proceedings of the 22nd USENIX conference on Security
Hi-index | 0.00 |
Current smartphone platforms provide ways for users to control access to information about their location. For instance, on the iPhone, when an application requests access to location information, the operating system asks the user whether to grant location access to this application. In this paper, we study how users are using these controls. Do iPhone users allow applications to access their location? Do their decisions differ from application to application? Can we predict how a user will respond for a particular application, given their past responses for other applications? We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users: some deny all applications access to their location, some allow all applications access to their location, and some selectively permit a fraction of their applications to access their location. We also find that apps can be separated into different classes by what fraction of users trust the app with their location data. Finally, we investigate using machine learning techniques to predict users' location-sharing decisions; we find that we are sometimes able to predict the user's actual choice, though there is considerable room for improvement. If it is possible to improve the accuracy rate further, this information could be used to relieve users of the cognitive burden of individually assigning location permissions for each application, allowing users to focus their attention on more critical matters.