Corpus ID: 10744615

User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models

@inproceedings{Cranshaw2011UserControllableLO,
  title={User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models},
  author={Justin Cranshaw and J. Mugan and N. Sadeh},
  booktitle={AAAI},
  year={2011}
}
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable… Expand
26 Citations
Crowdsourcing privacy preferences in context-aware applications
  • Eran Toch
  • Computer Science
  • Personal and Ubiquitous Computing
  • 2012
  • 59
  • PDF
Recommending privacy preferences in location-sharing services
The Persuasive Effect of Privacy Recommendations for Location Sharing Services
  • 9
Understanding and capturing people's mobile app privacy preferences
  • 20
  • PDF
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices
  • 61
  • PDF
Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions
  • 156
  • PDF
Privacy Challenges in Smart Devices
  • PDF
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings
  • 204
  • PDF
The Persuasive Effect of Privacy Recommendations
  • 14
Privacy dynamics: learning privacy norms for social software
  • PDF
...
1
2
3
...

References

SHOWING 1-10 OF 15 REFERENCES
Understanding and capturing people’s privacy policies in a mobile social networking application
  • 329
  • PDF
Privacy wizards for social networking sites
  • 447
  • PDF
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
  • 174
  • PDF
Location privacy in pervasive computing
  • 596
  • PDF
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
  • B. Gedik, L. Liu
  • Computer Science
  • IEEE Transactions on Mobile Computing
  • 2008
  • 846
  • PDF
Learning travel recommendations from user-generated GPS traces
  • 369
  • PDF
Location-Sharing Technologies: Privacy Risks and Controls
  • 172
  • PDF
Getting to know you: learning new user preferences in recommender systems
  • 578
  • PDF
...
1
2
...