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={J. Cranshaw and J. Mugan and N. Sadeh},
  booktitle={AAAI},
  year={2011}
}
  • J. Cranshaw, J. Mugan, N. Sadeh
  • Published in AAAI 2011
  • Computer Science
  • 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… CONTINUE READING
    Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings
    • 186
    • Open Access
    Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions
    • 134
    • Open Access
    Crowdsourcing privacy preferences in context-aware applications
    • 56
    • Open Access
    A comparative study of location-sharing privacy preferences in the United States and China
    • 36
    Privacy Dynamics: Learning Privacy Norms for Social Software
    • 27
    • Open Access
    Understanding and capturing people's mobile app privacy preferences
    • 19
    • Open Access
    The Persuasive Effect of Privacy Recommendations
    • 14
    The Persuasive Effect of Privacy Recommendations for Location Sharing Services
    • 8
    • Open Access

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 15 REFERENCES
    Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
    • 8,701
    • Open Access
    Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
    • 820
    • Open Access
    Privacy wizards for social networking sites
    • 435
    • Open Access
    Location privacy in pervasive computing
    • 545
    • Open Access
    Understanding and capturing people’s privacy policies in a mobile social networking application
    • 318
    • Open Access
    Learning travel recommendations from user-generated GPS traces
    • 356
    • Open Access
    Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
    • 171
    • Open Access
    Getting to know you: learning new user preferences in recommender systems
    • 563
    • Open Access
    Location-Sharing Technologies: Privacy Risks and Controls
    • 170
    • Open Access